From 184bc462e0b2c56c1db929f85982eca871bf210d Mon Sep 17 00:00:00 2001
From: s444417 <mikolaj12341230@gmail.com>
Date: Fri, 27 May 2022 18:01:18 +0200
Subject: [PATCH] DP happy path

---
 README.md                                     |   4 +-
 ...anie-dialogiem-reguly(zmodyfikowany).ipynb | 972 ++++++++++++++++++
 lab/09-zarzadzanie-dialogiem-reguly.ipynb     | 571 ++++------
 src/components/DP.py                          |  36 +-
 src/components/DST.py                         |  30 +-
 src/dialogue_system.py                        |   8 +-
 6 files changed, 1262 insertions(+), 359 deletions(-)
 create mode 100644 lab/09-zarzadzanie-dialogiem-reguly(zmodyfikowany).ipynb

diff --git a/README.md b/README.md
index b89af65..2ec0201 100644
--- a/README.md
+++ b/README.md
@@ -14,8 +14,10 @@
 
 ---
 
-## Zadanie 9/10 DST
+## Zadanie 9/10 DST i DP
 
 - implementacja regułowego modułu DST: **src/components DST.py**
 
+- implementacja regułowego DP, tylko happy path: **src/components DP.py**
+
 - wykorzystanie w pliku **src/dialogue_system.py**
diff --git a/lab/09-zarzadzanie-dialogiem-reguly(zmodyfikowany).ipynb b/lab/09-zarzadzanie-dialogiem-reguly(zmodyfikowany).ipynb
new file mode 100644
index 0000000..4032a32
--- /dev/null
+++ b/lab/09-zarzadzanie-dialogiem-reguly(zmodyfikowany).ipynb
@@ -0,0 +1,972 @@
+{
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "metadata": {
+                "collapsed": false
+            },
+            "source": [
+                "![Logo 1](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech1.jpg)\n",
+                "<div class=\"alert alert-block alert-info\">\n",
+                "<h1> Systemy Dialogowe </h1>\n",
+                "<h2> 9. <i>Zarządzanie dialogiem z wykorzystaniem reguł</i>  [laboratoria]</h2> \n",
+                "<h3> Marek Kubis (2021)</h3>\n",
+                "</div>\n",
+                "\n",
+                "![Logo 2](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech2.jpg)"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "Zarządzanie dialogiem z wykorzystaniem reguł\n",
+                "============================================\n",
+                "\n",
+                "Agent dialogowy wykorzystuje do zarządzanie dialogiem dwa moduły:\n",
+                "\n",
+                "  - monitor stanu dialogu (dialogue state tracker, DST) — moduł odpowiedzialny za śledzenie stanu dialogu.\n",
+                "\n",
+                "  - taktykę prowadzenia dialogu (dialogue policy) — moduł, który na podstawie stanu dialogu\n",
+                "    podejmuje decyzję o tym jaką akcję (akt systemu) agent ma podjąć w kolejnej turze.\n",
+                "\n",
+                "Oba moduły mogą być realizowane zarówno z wykorzystaniem reguł jak i uczenia maszynowego.\n",
+                "Mogą one zostać również połączone w pojedynczy moduł zwany wówczas *menedżerem dialogu*.\n",
+                "\n",
+                "Przykład\n",
+                "--------\n",
+                "\n",
+                "Zaimplementujemy regułowe moduły monitora stanu dialogu oraz taktyki dialogowej a następnie\n",
+                "osadzimy je w środowisku *[ConvLab-2](https://github.com/thu-coai/ConvLab-2)*,\n",
+                "które służy do ewaluacji systemów dialogowych.\n",
+                "\n",
+                "**Uwaga:** Niektóre moduły środowiska *ConvLab-2* nie są zgodne z najnowszymi wersjami Pythona,\n",
+                "dlatego przed uruchomieniem poniższych przykładów należy się upewnić, że mają Państwo interpreter\n",
+                "Pythona w wersji 3.7. W przypadku nowszych wersji Ubuntu Pythona 3.7 można zainstalować z\n",
+                "repozytorium `deadsnakes`, wykonując polecenia przedstawione poniżej.\n",
+                "\n",
+                "```\n",
+                "sudo add-apt-repository ppa:deadsnakes/ppa\n",
+                "sudo apt update\n",
+                "sudo apt install python3.7 python3.7-dev python3.7-venv\n",
+                "```\n",
+                "\n",
+                "W przypadku innych systemów można skorzystać np. z narzędzia [pyenv](https://github.com/pyenv/pyenv) lub środowiska [conda](https://conda.io).\n",
+                "\n",
+                "Ze względu na to, że *ConvLab-2* ma wiele zależności zachęcam również do skorzystania ze środowiska\n",
+                "wirtualnego `venv`, w którym moduły zależne mogą zostać zainstalowane.\n",
+                "W tym celu należy wykonać następujące polecenia\n",
+                "\n",
+                "```\n",
+                "python3.7 -m venv convenv                     # utworzenie nowego środowiska o nazwie convenv\n",
+                "source convenv/bin/activate                   # aktywacja środowiska w bieżącej powłoce\n",
+                "pip install --ignore-installed jupyter        # instalacja jupytera w środowisku convenv\n",
+                "```\n",
+                "\n",
+                "Po skonfigurowaniu środowiska można przystąpić do instalacji *ConvLab-2*, korzystając z\n",
+                "następujących poleceń\n",
+                "\n",
+                "```\n",
+                "mkdir -p l08\n",
+                "cd l08\n",
+                "git clone https://github.com/thu-coai/ConvLab-2.git\n",
+                "cd ConvLab-2\n",
+                "pip install -e .\n",
+                "python -m spacy download en_core_web_sm\n",
+                "cd ../..\n",
+                "```\n",
+                "\n",
+                "Po zakończeniu instalacji należy ponownie uruchomić notatnik w powłoce, w której aktywne jest\n",
+                "środowisko wirtualne *convenv*.\n",
+                "\n",
+                "```\n",
+                "jupyter notebook 08-zarzadzanie-dialogiem-reguly.ipynb\n",
+                "```\n",
+                "\n",
+                "Działanie zaimplementowanych modułów zilustrujemy, korzystając ze zbioru danych\n",
+                "[MultiWOZ](https://github.com/budzianowski/multiwoz) (Budzianowski i in., 2018), który zawiera\n",
+                "wypowiedzi dotyczące m.in. rezerwacji pokoi hotelowych, zamawiania biletów kolejowych oraz\n",
+                "rezerwacji stolików w restauracji.\n",
+                "\n",
+                "### Monitor Stanu Dialogu\n",
+                "\n",
+                "Do reprezentowania stanu dialogu użyjemy struktury danych wykorzystywanej w *ConvLab-2*."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 3,
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "text/plain": [
+                            "{'user_action': [],\n",
+                            " 'system_action': [],\n",
+                            " 'belief_state': {'cinema': {'book': {'title': '',\n",
+                            "    'date': '',\n",
+                            "    'time': '',\n",
+                            "    'quantity': '',\n",
+                            "    'seats': '',\n",
+                            "    'area': '',\n",
+                            "    'interval': ''},\n",
+                            "   'semi': {'goal': ''}}},\n",
+                            " 'request_state': {},\n",
+                            " 'terminated': False,\n",
+                            " 'history': []}"
+                        ]
+                    },
+                    "execution_count": 3,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "# from convlab2.util.multiwoz.state import default_state\n",
+                "from utils.state import default_state\n",
+                "default_state()"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "Metoda `update` naszego monitora stanu dialogu będzie przyjmować akty użytkownika i odpowiednio\n",
+                "modyfikować stan dialogu.\n",
+                "W przypadku aktów typu `inform` wartości slotów zostaną zapamiętane w słownikach odpowiadających\n",
+                "poszczególnym dziedzinom pod kluczem `belief_state`.\n",
+                "W przypadku aktów typu `request` sloty, o które pyta użytkownik zostaną zapisane pod kluczem\n",
+                "`request_state`.\n"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 34,
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "text/plain": [
+                            "\"\\nclass SimpleRuleDST(DST):\\n    def __init__(self):\\n        DST.__init__(self)\\n        self.state = default_state()\\n        self.value_dict = json.load(open('l08/ConvLab-2/data/multiwoz/value_dict.json'))\\n\\n    def update(self, user_act=None):\\n        for intent, domain, slot, value in user_act:\\n            domain = domain.lower()\\n            intent = intent.lower()\\n\\n            if domain in ['unk', 'general', 'booking']:\\n                continue\\n\\n            if intent == 'inform':\\n                k = REF_SYS_DA[domain.capitalize()].get(slot, slot)\\n\\n                if k is None:\\n                    continue\\n\\n                domain_dic = self.state['belief_state'][domain]\\n\\n                if k in domain_dic['semi']:\\n                    nvalue = normalize_value(self.value_dict, domain, k, value)\\n                    self.state['belief_state'][domain]['semi'][k] = nvalue\\n                elif k in domain_dic['book']:\\n                    self.state['belief_state'][domain]['book'][k] = value\\n                elif k.lower() in domain_dic['book']:\\n                    self.state['belief_state'][domain]['book'][k.lower()] = value\\n            elif intent == 'request':\\n                k = REF_SYS_DA[domain.capitalize()].get(slot, slot)\\n\\n                if domain not in self.state['request_state']:\\n                    self.state['request_state'][domain] = {}\\n                if k not in self.state['request_state'][domain]:\\n                    self.state['request_state'][domain][k] = 0\\n\\n        return self.state\\n\\n    def init_session(self):\\n        self.state = default_state()\\n\""
+                        ]
+                    },
+                    "execution_count": 34,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "import json\n",
+                "import os\n",
+                "from convlab2.dst.dst import DST\n",
+                "from convlab2.dst.rule.multiwoz.dst_util import normalize_value\n",
+                "from convlab2.util.multiwoz.multiwoz_slot_trans import REF_SYS_DA\n",
+                "\n",
+                "class SimpleRuleDST(DST):\n",
+                "    def __init__(self):\n",
+                "        DST.__init__(self)\n",
+                "        self.state = default_state()\n",
+                "        self.value_dict = json.load(open('utils/value_dict.json'))\n",
+                "\n",
+                "    def update(self, user_act=None):\n",
+                "        for intent, domain, slot, value in user_act:\n",
+                "            domain = domain.lower()\n",
+                "            intent = intent.lower()\n",
+                "            value = value.lower()\n",
+                "\n",
+                "            # if domain in ['unk', 'cinema']:\n",
+                "            #     continue\n",
+                "            k = slot\n",
+                "\n",
+                "            if intent == 'inform':\n",
+                "                # k = REF_SYS_DA[domain.capitalize()].get(slot, slot)\n",
+                "                # if k is None:\n",
+                "                #     continue\n",
+                "\n",
+                "                domain_dic = self.state['belief_state'][domain]\n",
+                "\n",
+                "                if k in domain_dic['semi']:\n",
+                "                    # nvalue = normalize_value(self.value_dict, domain, k, value)\n",
+                "                    self.state['belief_state'][domain]['semi'][k] = value\n",
+                "                elif k in domain_dic['book']:\n",
+                "                    self.state['belief_state'][domain]['book'][k] = value\n",
+                "                elif k.lower() in domain_dic['book']:\n",
+                "                    self.state['belief_state'][domain]['book'][k.lower()] = value\n",
+                "            elif intent == 'request':\n",
+                "                # k = REF_SYS_DA[domain.capitalize()].get(slot, slot)\n",
+                "\n",
+                "                if domain not in self.state['request_state']:\n",
+                "                    self.state['request_state'][domain] = {}\n",
+                "                if k not in self.state['request_state'][domain]:\n",
+                "                    self.state['request_state'][domain][k] = 0\n",
+                "\n",
+                "        return self.state\n",
+                "\n",
+                "    def init_session(self):\n",
+                "        self.state = default_state()\n",
+                "\n",
+                "'''\n",
+                "class SimpleRuleDST(DST):\n",
+                "    def __init__(self):\n",
+                "        DST.__init__(self)\n",
+                "        self.state = default_state()\n",
+                "        self.value_dict = json.load(open('l08/ConvLab-2/data/multiwoz/value_dict.json'))\n",
+                "\n",
+                "    def update(self, user_act=None):\n",
+                "        for intent, domain, slot, value in user_act:\n",
+                "            domain = domain.lower()\n",
+                "            intent = intent.lower()\n",
+                "\n",
+                "            if domain in ['unk', 'general', 'booking']:\n",
+                "                continue\n",
+                "\n",
+                "            if intent == 'inform':\n",
+                "                k = REF_SYS_DA[domain.capitalize()].get(slot, slot)\n",
+                "\n",
+                "                if k is None:\n",
+                "                    continue\n",
+                "\n",
+                "                domain_dic = self.state['belief_state'][domain]\n",
+                "\n",
+                "                if k in domain_dic['semi']:\n",
+                "                    nvalue = normalize_value(self.value_dict, domain, k, value)\n",
+                "                    self.state['belief_state'][domain]['semi'][k] = nvalue\n",
+                "                elif k in domain_dic['book']:\n",
+                "                    self.state['belief_state'][domain]['book'][k] = value\n",
+                "                elif k.lower() in domain_dic['book']:\n",
+                "                    self.state['belief_state'][domain]['book'][k.lower()] = value\n",
+                "            elif intent == 'request':\n",
+                "                k = REF_SYS_DA[domain.capitalize()].get(slot, slot)\n",
+                "\n",
+                "                if domain not in self.state['request_state']:\n",
+                "                    self.state['request_state'][domain] = {}\n",
+                "                if k not in self.state['request_state'][domain]:\n",
+                "                    self.state['request_state'][domain][k] = 0\n",
+                "\n",
+                "        return self.state\n",
+                "\n",
+                "    def init_session(self):\n",
+                "        self.state = default_state()\n",
+                "'''\n"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "W definicji metody `update` zakładamy, że akty dialogowe przekazywane do monitora stanu dialogu z\n",
+                "modułu NLU są czteroelementowymi listami złożonymi z:\n",
+                "\n",
+                " - nazwy aktu użytkownika,\n",
+                " - nazwy dziedziny, której dotyczy wypowiedź,\n",
+                " - nazwy slotu,\n",
+                " - wartości slotu.\n",
+                "\n",
+                "Zobaczmy na kilku prostych przykładach jak stan dialogu zmienia się pod wpływem przekazanych aktów\n",
+                "użytkownika."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 35,
+            "metadata": {
+                "lines_to_next_cell": 0
+            },
+            "outputs": [
+                {
+                    "data": {
+                        "text/plain": [
+                            "{'user_action': [],\n",
+                            " 'system_action': [],\n",
+                            " 'belief_state': {'cinema': {'book': {'title': '',\n",
+                            "    'date': '',\n",
+                            "    'time': '',\n",
+                            "    'quantity': '',\n",
+                            "    'seats': '',\n",
+                            "    'area': '',\n",
+                            "    'interval': ''},\n",
+                            "   'semi': {'goal': ''}}},\n",
+                            " 'request_state': {},\n",
+                            " 'terminated': False,\n",
+                            " 'history': []}"
+                        ]
+                    },
+                    "execution_count": 35,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "dst = SimpleRuleDST()\n",
+                "dst.state"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 36,
+            "metadata": {
+                "lines_to_next_cell": 0
+            },
+            "outputs": [
+                {
+                    "data": {
+                        "text/plain": [
+                            "{'book': {'title': 'batman',\n",
+                            "  'date': '',\n",
+                            "  'time': '15:00',\n",
+                            "  'quantity': '',\n",
+                            "  'seats': '',\n",
+                            "  'area': '',\n",
+                            "  'interval': ''},\n",
+                            " 'semi': {'goal': ''}}"
+                        ]
+                    },
+                    "execution_count": 36,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "dst.update([['Inform', 'Cinema', 'time', '15:00'], ['Inform', 'Cinema', 'title', 'Batman']])\n",
+                "dst.state['belief_state']['cinema']"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 37,
+            "metadata": {
+                "lines_to_next_cell": 0
+            },
+            "outputs": [
+                {
+                    "data": {
+                        "text/plain": [
+                            "{'book': {'title': 'batman',\n",
+                            "  'date': '',\n",
+                            "  'time': '15:00',\n",
+                            "  'quantity': '',\n",
+                            "  'seats': '',\n",
+                            "  'area': 'na górze na środku',\n",
+                            "  'interval': ''},\n",
+                            " 'semi': {'goal': ''}}"
+                        ]
+                    },
+                    "execution_count": 37,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "dst.update([['Inform', 'Cinema', 'area', 'na górze na środku']])\n",
+                "dst.state['belief_state']['cinema']"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 38,
+            "metadata": {
+                "lines_to_next_cell": 0
+            },
+            "outputs": [
+                {
+                    "data": {
+                        "text/plain": [
+                            "{'cinema': {'date': 0}}"
+                        ]
+                    },
+                    "execution_count": 38,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "dst.update([['Request', 'Cinema', 'date', '?']])\n",
+                "dst.state['request_state']"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {
+                "lines_to_next_cell": 0
+            },
+            "outputs": [
+                {
+                    "ename": "",
+                    "evalue": "",
+                    "output_type": "error",
+                    "traceback": [
+                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
+                        "Run the following command to install 'ipykernel' into the Python environment. \n",
+                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
+                    ]
+                },
+                {
+                    "ename": "",
+                    "evalue": "",
+                    "output_type": "error",
+                    "traceback": [
+                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
+                        "Run the following command to install 'ipykernel' into the Python environment. \n",
+                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
+                    ]
+                }
+            ],
+            "source": [
+                "dst.update([['Inform', 'Hotel', 'Day', 'tuesday'], ['Inform', 'Hotel', 'People', '2'], ['Inform', 'Hotel', 'Stay', '4']])\n",
+                "dst.state['belief_state']['hotel']"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": 39,
+            "metadata": {},
+            "outputs": [
+                {
+                    "data": {
+                        "text/plain": [
+                            "{'user_action': [],\n",
+                            " 'system_action': [],\n",
+                            " 'belief_state': {'cinema': {'book': {'title': 'batman',\n",
+                            "    'date': '',\n",
+                            "    'time': '15:00',\n",
+                            "    'quantity': '',\n",
+                            "    'seats': '',\n",
+                            "    'area': 'na górze na środku',\n",
+                            "    'interval': ''},\n",
+                            "   'semi': {'goal': ''}}},\n",
+                            " 'request_state': {'cinema': {'date': 0}},\n",
+                            " 'terminated': False,\n",
+                            " 'history': []}"
+                        ]
+                    },
+                    "execution_count": 39,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                }
+            ],
+            "source": [
+                "dst.state"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "### Taktyka Prowadzenia Dialogu\n",
+                "\n",
+                "Prosta taktyka prowadzenia dialogu dla systemu rezerwacji pokoi hotelowych może składać się z następujących reguł:\n",
+                "\n",
+                " 1. Jeżeli użytkownik przekazał w ostatniej turze akt typu `Request`, to udziel odpowiedzi na jego\n",
+                "    pytanie.\n",
+                "\n",
+                " 2. Jeżeli użytkownik przekazał w ostatniej turze akt typu `Inform`, to zaproponuj mu hotel\n",
+                "    spełniający zdefiniowane przez niego kryteria.\n",
+                "\n",
+                " 3. Jeżeli użytkownik przekazał w ostatniej turze akt typu `Inform` zawierający szczegóły\n",
+                "    rezerwacji, to zarezerwuj pokój.\n",
+                "\n",
+                "Metoda `predict` taktyki `SimpleRulePolicy` realizuje reguły przedstawione powyżej."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [
+                {
+                    "ename": "",
+                    "evalue": "",
+                    "output_type": "error",
+                    "traceback": [
+                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
+                        "Run the following command to install 'ipykernel' into the Python environment. \n",
+                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
+                    ]
+                },
+                {
+                    "ename": "",
+                    "evalue": "",
+                    "output_type": "error",
+                    "traceback": [
+                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
+                        "Run the following command to install 'ipykernel' into the Python environment. \n",
+                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
+                    ]
+                }
+            ],
+            "source": [
+                "from collections import defaultdict\n",
+                "import copy\n",
+                "import json\n",
+                "from copy import deepcopy\n",
+                "\n",
+                "from convlab2.policy.policy import Policy\n",
+                "from convlab2.util.multiwoz.dbquery import Database\n",
+                "from convlab2.util.multiwoz.multiwoz_slot_trans import REF_SYS_DA, REF_USR_DA\n",
+                "\n",
+                "\n",
+                "class SimpleRulePolicy(Policy):\n",
+                "    def __init__(self):\n",
+                "        Policy.__init__(self)\n",
+                "        self.db = Database()\n",
+                "\n",
+                "    def predict(self, state):\n",
+                "        self.results = []\n",
+                "        system_action = defaultdict(list)\n",
+                "        user_action = defaultdict(list)\n",
+                "\n",
+                "        for intent, domain, slot, value in state['user_action']:\n",
+                "            user_action[(domain, intent)].append((slot, value))\n",
+                "\n",
+                "        for user_act in user_action:\n",
+                "            self.update_system_action(user_act, user_action, state, system_action)\n",
+                "\n",
+                "        # Reguła 3\n",
+                "        if any(True for slots in user_action.values() for (slot, _) in slots if slot in ['Stay', 'Day', 'People']):\n",
+                "            if self.results:\n",
+                "                system_action = {('Booking', 'Book'): [[\"Ref\", self.results[0].get('Ref', 'N/A')]]}\n",
+                "\n",
+                "        system_acts = [[intent, domain, slot, value] for (domain, intent), slots in system_action.items() for slot, value in slots]\n",
+                "        state['system_action'] = system_acts\n",
+                "        return system_acts\n",
+                "\n",
+                "    def update_system_action(self, user_act, user_action, state, system_action):\n",
+                "        domain, intent = user_act\n",
+                "        constraints = [(slot, value) for slot, value in state['belief_state'][domain.lower()]['semi'].items() if value != '']\n",
+                "        self.results = deepcopy(self.db.query(domain.lower(), constraints))\n",
+                "\n",
+                "        # Reguła 1\n",
+                "        if intent == 'Request':\n",
+                "            if len(self.results) == 0:\n",
+                "                system_action[(domain, 'NoOffer')] = []\n",
+                "            else:\n",
+                "                for slot in user_action[user_act]:\n",
+                "                    kb_slot_name = REF_SYS_DA[domain].get(slot[0], slot[0])\n",
+                "\n",
+                "                    if kb_slot_name in self.results[0]:\n",
+                "                        system_action[(domain, 'Inform')].append([slot[0], self.results[0].get(kb_slot_name, 'unknown')])\n",
+                "\n",
+                "        # Reguła 2\n",
+                "        elif intent == 'Inform':\n",
+                "            if len(self.results) == 0:\n",
+                "                system_action[(domain, 'NoOffer')] = []\n",
+                "            else:\n",
+                "                system_action[(domain, 'Inform')].append(['Choice', str(len(self.results))])\n",
+                "                choice = self.results[0]\n",
+                "\n",
+                "                if domain in [\"Hotel\", \"Attraction\", \"Police\", \"Restaurant\"]:\n",
+                "                    system_action[(domain, 'Recommend')].append(['Name', choice['name']])"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "Podobnie jak w przypadku aktów użytkownika akty systemowe przekazywane do modułu NLG są czteroelementowymi listami złożonymi z:\n",
+                "\n",
+                " - nazwy aktu systemowe,\n",
+                " - nazwy dziedziny, której dotyczy wypowiedź,\n",
+                " - nazwy slotu,\n",
+                " - wartości slotu.\n",
+                "\n",
+                "Sprawdźmy jakie akty systemowe zwraca taktyka `SimpleRulePolicy` w odpowiedzi na zmieniający się stan dialogu."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {
+                "lines_to_next_cell": 0
+            },
+            "outputs": [
+                {
+                    "ename": "",
+                    "evalue": "",
+                    "output_type": "error",
+                    "traceback": [
+                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
+                        "Run the following command to install 'ipykernel' into the Python environment. \n",
+                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
+                    ]
+                },
+                {
+                    "ename": "",
+                    "evalue": "",
+                    "output_type": "error",
+                    "traceback": [
+                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
+                        "Run the following command to install 'ipykernel' into the Python environment. \n",
+                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
+                    ]
+                }
+            ],
+            "source": [
+                "from convlab2.dialog_agent import PipelineAgent\n",
+                "dst.init_session()\n",
+                "policy = SimpleRulePolicy()\n",
+                "agent = PipelineAgent(nlu=None, dst=dst, policy=policy, nlg=None, name='sys')"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {
+                "lines_to_next_cell": 0
+            },
+            "outputs": [
+                {
+                    "ename": "",
+                    "evalue": "",
+                    "output_type": "error",
+                    "traceback": [
+                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
+                        "Run the following command to install 'ipykernel' into the Python environment. \n",
+                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
+                    ]
+                },
+                {
+                    "ename": "",
+                    "evalue": "",
+                    "output_type": "error",
+                    "traceback": [
+                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
+                        "Run the following command to install 'ipykernel' into the Python environment. \n",
+                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
+                    ]
+                }
+            ],
+            "source": [
+                "agent.response([['Inform', 'Hotel', 'Price', 'cheap'], ['Inform', 'Hotel', 'Parking', 'yes']])"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {
+                "lines_to_next_cell": 0
+            },
+            "outputs": [
+                {
+                    "ename": "",
+                    "evalue": "",
+                    "output_type": "error",
+                    "traceback": [
+                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
+                        "Run the following command to install 'ipykernel' into the Python environment. \n",
+                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
+                    ]
+                },
+                {
+                    "ename": "",
+                    "evalue": "",
+                    "output_type": "error",
+                    "traceback": [
+                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
+                        "Run the following command to install 'ipykernel' into the Python environment. \n",
+                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
+                    ]
+                }
+            ],
+            "source": [
+                "agent.response([['Inform', 'Hotel', 'Area', 'north']])"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {
+                "lines_to_next_cell": 0
+            },
+            "outputs": [
+                {
+                    "ename": "",
+                    "evalue": "",
+                    "output_type": "error",
+                    "traceback": [
+                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
+                        "Run the following command to install 'ipykernel' into the Python environment. \n",
+                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
+                    ]
+                },
+                {
+                    "ename": "",
+                    "evalue": "",
+                    "output_type": "error",
+                    "traceback": [
+                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
+                        "Run the following command to install 'ipykernel' into the Python environment. \n",
+                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
+                    ]
+                }
+            ],
+            "source": [
+                "agent.response([['Request', 'Hotel', 'Area', '?']])"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [
+                {
+                    "ename": "",
+                    "evalue": "",
+                    "output_type": "error",
+                    "traceback": [
+                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
+                        "Run the following command to install 'ipykernel' into the Python environment. \n",
+                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
+                    ]
+                },
+                {
+                    "ename": "",
+                    "evalue": "",
+                    "output_type": "error",
+                    "traceback": [
+                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
+                        "Run the following command to install 'ipykernel' into the Python environment. \n",
+                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
+                    ]
+                }
+            ],
+            "source": [
+                "agent.response([['Inform', 'Hotel', 'Day', 'tuesday'], ['Inform', 'Hotel', 'People', '2'], ['Inform', 'Hotel', 'Stay', '4']])"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "### Testy End-to-End\n",
+                "\n",
+                "Na koniec przeprowadźmy dialog łącząc w potok nasze moduły\n",
+                "z modułami NLU i NLG dostępnymi dla MultiWOZ w środowisku `ConvLab-2`."
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [
+                {
+                    "ename": "",
+                    "evalue": "",
+                    "output_type": "error",
+                    "traceback": [
+                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
+                        "Run the following command to install 'ipykernel' into the Python environment. \n",
+                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
+                    ]
+                },
+                {
+                    "ename": "",
+                    "evalue": "",
+                    "output_type": "error",
+                    "traceback": [
+                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
+                        "Run the following command to install 'ipykernel' into the Python environment. \n",
+                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
+                    ]
+                }
+            ],
+            "source": [
+                "from convlab2.nlu.svm.multiwoz import SVMNLU\n",
+                "from convlab2.nlg.template.multiwoz import TemplateNLG\n",
+                "\n",
+                "nlu = SVMNLU()\n",
+                "nlg = TemplateNLG(is_user=False)\n",
+                "agent = PipelineAgent(nlu=nlu, dst=dst, policy=policy, nlg=nlg, name='sys')"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {
+                "lines_to_next_cell": 0
+            },
+            "outputs": [
+                {
+                    "ename": "",
+                    "evalue": "",
+                    "output_type": "error",
+                    "traceback": [
+                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
+                        "Run the following command to install 'ipykernel' into the Python environment. \n",
+                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
+                    ]
+                },
+                {
+                    "ename": "",
+                    "evalue": "",
+                    "output_type": "error",
+                    "traceback": [
+                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
+                        "Run the following command to install 'ipykernel' into the Python environment. \n",
+                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
+                    ]
+                }
+            ],
+            "source": [
+                "agent.response(\"I need a cheap hotel with free parking .\")"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {
+                "lines_to_next_cell": 0
+            },
+            "outputs": [
+                {
+                    "ename": "",
+                    "evalue": "",
+                    "output_type": "error",
+                    "traceback": [
+                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
+                        "Run the following command to install 'ipykernel' into the Python environment. \n",
+                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
+                    ]
+                },
+                {
+                    "ename": "",
+                    "evalue": "",
+                    "output_type": "error",
+                    "traceback": [
+                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
+                        "Run the following command to install 'ipykernel' into the Python environment. \n",
+                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
+                    ]
+                }
+            ],
+            "source": [
+                "agent.response(\"Where it is located ?\")"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {
+                "lines_to_next_cell": 0
+            },
+            "outputs": [
+                {
+                    "ename": "",
+                    "evalue": "",
+                    "output_type": "error",
+                    "traceback": [
+                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
+                        "Run the following command to install 'ipykernel' into the Python environment. \n",
+                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
+                    ]
+                },
+                {
+                    "ename": "",
+                    "evalue": "",
+                    "output_type": "error",
+                    "traceback": [
+                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
+                        "Run the following command to install 'ipykernel' into the Python environment. \n",
+                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
+                    ]
+                }
+            ],
+            "source": [
+                "agent.response(\"I would prefer the hotel be in the north part of town .\")"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [
+                {
+                    "ename": "",
+                    "evalue": "",
+                    "output_type": "error",
+                    "traceback": [
+                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
+                        "Run the following command to install 'ipykernel' into the Python environment. \n",
+                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
+                    ]
+                },
+                {
+                    "ename": "",
+                    "evalue": "",
+                    "output_type": "error",
+                    "traceback": [
+                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
+                        "Run the following command to install 'ipykernel' into the Python environment. \n",
+                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
+                    ]
+                }
+            ],
+            "source": [
+                "agent.response(\"Yeah , could you book me a room for 2 people for 4 nights starting Tuesday ?\")"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "Zauważmy, ze nasza prosta taktyka dialogowa zawiera wiele luk, do których należą m.in.:\n",
+                "\n",
+                " 1. Niezdolność do udzielenia odpowiedzi na przywitanie, prośbę o pomoc lub restart.\n",
+                "\n",
+                " 2. Brak reguł dopytujących użytkownika o szczegóły niezbędne do dokonania rezerwacji takie, jak długość pobytu czy liczba osób.\n",
+                "\n",
+                "Bardziej zaawansowane moduły zarządzania dialogiem zbudowane z wykorzystaniem reguł można znaleźć w\n",
+                "środowisku `ConvLab-2`. Należą do nich m.in. monitor [RuleDST](https://github.com/thu-coai/ConvLab-2/blob/master/convlab2/dst/rule/multiwoz/dst.py) oraz taktyka [RuleBasedMultiwozBot](https://github.com/thu-coai/ConvLab-2/blob/master/convlab2/policy/rule/multiwoz/rule_based_multiwoz_bot.py).\n",
+                "\n",
+                "Zadania\n",
+                "-------\n",
+                " 1. Zaimplementować w projekcie monitor stanu dialogu.\n",
+                "\n",
+                " 2. Zaimplementować w projekcie taktykę prowadzenia dialogu.\n",
+                "\n",
+                "Termin: 24.05.2021, godz. 23:59.\n",
+                "\n",
+                "Literatura\n",
+                "----------\n",
+                " 1. Pawel Budzianowski, Tsung-Hsien Wen, Bo-Hsiang Tseng, Iñigo Casanueva, Stefan Ultes, Osman Ramadan, Milica Gasic, MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling. EMNLP 2018, pp. 5016-5026\n",
+                " 2. Cathy Pearl, Basic principles for designing voice user interfaces, https://www.oreilly.com/content/basic-principles-for-designing-voice-user-interfaces/ data dostępu: 21 marca 2021\n",
+                " 3. Cathy Pearl, Designing Voice User Interfaces, Excerpts from Chapter 5: Advanced Voice User Interface Design, https://www.uxmatters.com/mt/archives/2018/01/designing-voice-user-interfaces.php data dostępu: 21 marca 2021"
+            ]
+        }
+    ],
+    "metadata": {
+        "author": "Marek Kubis",
+        "email": "mkubis@amu.edu.pl",
+        "interpreter": {
+            "hash": "91e2b0d1baa6ebb76863bdb1d11380bf032a6a1fc1b919194f041a5133852891"
+        },
+        "jupytext": {
+            "cell_metadata_filter": "-all",
+            "main_language": "python",
+            "notebook_metadata_filter": "-all"
+        },
+        "kernelspec": {
+            "display_name": "Python 3.7.9 ('venv': venv)",
+            "language": "python",
+            "name": "python3"
+        },
+        "lang": "pl",
+        "language_info": {
+            "codemirror_mode": {
+                "name": "ipython",
+                "version": 3
+            },
+            "file_extension": ".py",
+            "mimetype": "text/x-python",
+            "name": "python",
+            "nbconvert_exporter": "python",
+            "pygments_lexer": "ipython3",
+            "version": "3.7.9"
+        },
+        "subtitle": "9.Zarządzanie dialogiem z wykorzystaniem reguł[laboratoria]",
+        "title": "Systemy Dialogowe",
+        "year": "2021"
+    },
+    "nbformat": 4,
+    "nbformat_minor": 4
+}
diff --git a/lab/09-zarzadzanie-dialogiem-reguly.ipynb b/lab/09-zarzadzanie-dialogiem-reguly.ipynb
index 4032a32..bce9113 100644
--- a/lab/09-zarzadzanie-dialogiem-reguly.ipynb
+++ b/lab/09-zarzadzanie-dialogiem-reguly.ipynb
@@ -95,7 +95,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 3,
+            "execution_count": 1,
             "metadata": {},
             "outputs": [
                 {
@@ -103,27 +103,43 @@
                         "text/plain": [
                             "{'user_action': [],\n",
                             " 'system_action': [],\n",
-                            " 'belief_state': {'cinema': {'book': {'title': '',\n",
-                            "    'date': '',\n",
-                            "    'time': '',\n",
-                            "    'quantity': '',\n",
-                            "    'seats': '',\n",
+                            " 'belief_state': {'police': {'book': {'booked': []}, 'semi': {}},\n",
+                            "  'hotel': {'book': {'booked': [], 'people': '', 'day': '', 'stay': ''},\n",
+                            "   'semi': {'name': '',\n",
                             "    'area': '',\n",
-                            "    'interval': ''},\n",
-                            "   'semi': {'goal': ''}}},\n",
+                            "    'parking': '',\n",
+                            "    'pricerange': '',\n",
+                            "    'stars': '',\n",
+                            "    'internet': '',\n",
+                            "    'type': ''}},\n",
+                            "  'attraction': {'book': {'booked': []},\n",
+                            "   'semi': {'type': '', 'name': '', 'area': ''}},\n",
+                            "  'restaurant': {'book': {'booked': [], 'people': '', 'day': '', 'time': ''},\n",
+                            "   'semi': {'food': '', 'pricerange': '', 'name': '', 'area': ''}},\n",
+                            "  'hospital': {'book': {'booked': []}, 'semi': {'department': ''}},\n",
+                            "  'taxi': {'book': {'booked': []},\n",
+                            "   'semi': {'leaveAt': '',\n",
+                            "    'destination': '',\n",
+                            "    'departure': '',\n",
+                            "    'arriveBy': ''}},\n",
+                            "  'train': {'book': {'booked': [], 'people': ''},\n",
+                            "   'semi': {'leaveAt': '',\n",
+                            "    'destination': '',\n",
+                            "    'day': '',\n",
+                            "    'arriveBy': '',\n",
+                            "    'departure': ''}}},\n",
                             " 'request_state': {},\n",
                             " 'terminated': False,\n",
                             " 'history': []}"
                         ]
                     },
-                    "execution_count": 3,
+                    "execution_count": 1,
                     "metadata": {},
                     "output_type": "execute_result"
                 }
             ],
             "source": [
-                "# from convlab2.util.multiwoz.state import default_state\n",
-                "from utils.state import default_state\n",
+                "from convlab2.util.multiwoz.state import default_state\n",
                 "default_state()"
             ]
         },
@@ -141,20 +157,9 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 34,
+            "execution_count": 2,
             "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "\"\\nclass SimpleRuleDST(DST):\\n    def __init__(self):\\n        DST.__init__(self)\\n        self.state = default_state()\\n        self.value_dict = json.load(open('l08/ConvLab-2/data/multiwoz/value_dict.json'))\\n\\n    def update(self, user_act=None):\\n        for intent, domain, slot, value in user_act:\\n            domain = domain.lower()\\n            intent = intent.lower()\\n\\n            if domain in ['unk', 'general', 'booking']:\\n                continue\\n\\n            if intent == 'inform':\\n                k = REF_SYS_DA[domain.capitalize()].get(slot, slot)\\n\\n                if k is None:\\n                    continue\\n\\n                domain_dic = self.state['belief_state'][domain]\\n\\n                if k in domain_dic['semi']:\\n                    nvalue = normalize_value(self.value_dict, domain, k, value)\\n                    self.state['belief_state'][domain]['semi'][k] = nvalue\\n                elif k in domain_dic['book']:\\n                    self.state['belief_state'][domain]['book'][k] = value\\n                elif k.lower() in domain_dic['book']:\\n                    self.state['belief_state'][domain]['book'][k.lower()] = value\\n            elif intent == 'request':\\n                k = REF_SYS_DA[domain.capitalize()].get(slot, slot)\\n\\n                if domain not in self.state['request_state']:\\n                    self.state['request_state'][domain] = {}\\n                if k not in self.state['request_state'][domain]:\\n                    self.state['request_state'][domain][k] = 0\\n\\n        return self.state\\n\\n    def init_session(self):\\n        self.state = default_state()\\n\""
-                        ]
-                    },
-                    "execution_count": 34,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
+            "outputs": [],
             "source": [
                 "import json\n",
                 "import os\n",
@@ -162,55 +167,12 @@
                 "from convlab2.dst.rule.multiwoz.dst_util import normalize_value\n",
                 "from convlab2.util.multiwoz.multiwoz_slot_trans import REF_SYS_DA\n",
                 "\n",
+                "\n",
                 "class SimpleRuleDST(DST):\n",
                 "    def __init__(self):\n",
                 "        DST.__init__(self)\n",
                 "        self.state = default_state()\n",
-                "        self.value_dict = json.load(open('utils/value_dict.json'))\n",
-                "\n",
-                "    def update(self, user_act=None):\n",
-                "        for intent, domain, slot, value in user_act:\n",
-                "            domain = domain.lower()\n",
-                "            intent = intent.lower()\n",
-                "            value = value.lower()\n",
-                "\n",
-                "            # if domain in ['unk', 'cinema']:\n",
-                "            #     continue\n",
-                "            k = slot\n",
-                "\n",
-                "            if intent == 'inform':\n",
-                "                # k = REF_SYS_DA[domain.capitalize()].get(slot, slot)\n",
-                "                # if k is None:\n",
-                "                #     continue\n",
-                "\n",
-                "                domain_dic = self.state['belief_state'][domain]\n",
-                "\n",
-                "                if k in domain_dic['semi']:\n",
-                "                    # nvalue = normalize_value(self.value_dict, domain, k, value)\n",
-                "                    self.state['belief_state'][domain]['semi'][k] = value\n",
-                "                elif k in domain_dic['book']:\n",
-                "                    self.state['belief_state'][domain]['book'][k] = value\n",
-                "                elif k.lower() in domain_dic['book']:\n",
-                "                    self.state['belief_state'][domain]['book'][k.lower()] = value\n",
-                "            elif intent == 'request':\n",
-                "                # k = REF_SYS_DA[domain.capitalize()].get(slot, slot)\n",
-                "\n",
-                "                if domain not in self.state['request_state']:\n",
-                "                    self.state['request_state'][domain] = {}\n",
-                "                if k not in self.state['request_state'][domain]:\n",
-                "                    self.state['request_state'][domain][k] = 0\n",
-                "\n",
-                "        return self.state\n",
-                "\n",
-                "    def init_session(self):\n",
-                "        self.state = default_state()\n",
-                "\n",
-                "'''\n",
-                "class SimpleRuleDST(DST):\n",
-                "    def __init__(self):\n",
-                "        DST.__init__(self)\n",
-                "        self.state = default_state()\n",
-                "        self.value_dict = json.load(open('l08/ConvLab-2/data/multiwoz/value_dict.json'))\n",
+                "        self.value_dict = json.load(open('ConvLab-2/data/multiwoz/value_dict.json'))\n",
                 "\n",
                 "    def update(self, user_act=None):\n",
                 "        for intent, domain, slot, value in user_act:\n",
@@ -246,8 +208,7 @@
                 "        return self.state\n",
                 "\n",
                 "    def init_session(self):\n",
-                "        self.state = default_state()\n",
-                "'''\n"
+                "        self.state = default_state()\n"
             ]
         },
         {
@@ -268,7 +229,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 35,
+            "execution_count": 3,
             "metadata": {
                 "lines_to_next_cell": 0
             },
@@ -278,20 +239,37 @@
                         "text/plain": [
                             "{'user_action': [],\n",
                             " 'system_action': [],\n",
-                            " 'belief_state': {'cinema': {'book': {'title': '',\n",
-                            "    'date': '',\n",
-                            "    'time': '',\n",
-                            "    'quantity': '',\n",
-                            "    'seats': '',\n",
+                            " 'belief_state': {'police': {'book': {'booked': []}, 'semi': {}},\n",
+                            "  'hotel': {'book': {'booked': [], 'people': '', 'day': '', 'stay': ''},\n",
+                            "   'semi': {'name': '',\n",
                             "    'area': '',\n",
-                            "    'interval': ''},\n",
-                            "   'semi': {'goal': ''}}},\n",
+                            "    'parking': '',\n",
+                            "    'pricerange': '',\n",
+                            "    'stars': '',\n",
+                            "    'internet': '',\n",
+                            "    'type': ''}},\n",
+                            "  'attraction': {'book': {'booked': []},\n",
+                            "   'semi': {'type': '', 'name': '', 'area': ''}},\n",
+                            "  'restaurant': {'book': {'booked': [], 'people': '', 'day': '', 'time': ''},\n",
+                            "   'semi': {'food': '', 'pricerange': '', 'name': '', 'area': ''}},\n",
+                            "  'hospital': {'book': {'booked': []}, 'semi': {'department': ''}},\n",
+                            "  'taxi': {'book': {'booked': []},\n",
+                            "   'semi': {'leaveAt': '',\n",
+                            "    'destination': '',\n",
+                            "    'departure': '',\n",
+                            "    'arriveBy': ''}},\n",
+                            "  'train': {'book': {'booked': [], 'people': ''},\n",
+                            "   'semi': {'leaveAt': '',\n",
+                            "    'destination': '',\n",
+                            "    'day': '',\n",
+                            "    'arriveBy': '',\n",
+                            "    'departure': ''}}},\n",
                             " 'request_state': {},\n",
                             " 'terminated': False,\n",
                             " 'history': []}"
                         ]
                     },
-                    "execution_count": 35,
+                    "execution_count": 3,
                     "metadata": {},
                     "output_type": "execute_result"
                 }
@@ -303,7 +281,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 36,
+            "execution_count": 4,
             "metadata": {
                 "lines_to_next_cell": 0
             },
@@ -311,29 +289,29 @@
                 {
                     "data": {
                         "text/plain": [
-                            "{'book': {'title': 'batman',\n",
-                            "  'date': '',\n",
-                            "  'time': '15:00',\n",
-                            "  'quantity': '',\n",
-                            "  'seats': '',\n",
+                            "{'book': {'booked': [], 'people': '', 'day': '', 'stay': ''},\n",
+                            " 'semi': {'name': '',\n",
                             "  'area': '',\n",
-                            "  'interval': ''},\n",
-                            " 'semi': {'goal': ''}}"
+                            "  'parking': 'yes',\n",
+                            "  'pricerange': 'cheap',\n",
+                            "  'stars': '',\n",
+                            "  'internet': '',\n",
+                            "  'type': ''}}"
                         ]
                     },
-                    "execution_count": 36,
+                    "execution_count": 4,
                     "metadata": {},
                     "output_type": "execute_result"
                 }
             ],
             "source": [
-                "dst.update([['Inform', 'Cinema', 'time', '15:00'], ['Inform', 'Cinema', 'title', 'Batman']])\n",
-                "dst.state['belief_state']['cinema']"
+                "dst.update([['Inform', 'Hotel', 'Price', 'cheap'], ['Inform', 'Hotel', 'Parking', 'yes']])\n",
+                "dst.state['belief_state']['hotel']"
             ]
         },
         {
             "cell_type": "code",
-            "execution_count": 37,
+            "execution_count": 5,
             "metadata": {
                 "lines_to_next_cell": 0
             },
@@ -341,29 +319,29 @@
                 {
                     "data": {
                         "text/plain": [
-                            "{'book': {'title': 'batman',\n",
-                            "  'date': '',\n",
-                            "  'time': '15:00',\n",
-                            "  'quantity': '',\n",
-                            "  'seats': '',\n",
-                            "  'area': 'na górze na środku',\n",
-                            "  'interval': ''},\n",
-                            " 'semi': {'goal': ''}}"
+                            "{'book': {'booked': [], 'people': '', 'day': '', 'stay': ''},\n",
+                            " 'semi': {'name': '',\n",
+                            "  'area': 'north',\n",
+                            "  'parking': 'yes',\n",
+                            "  'pricerange': 'cheap',\n",
+                            "  'stars': '',\n",
+                            "  'internet': '',\n",
+                            "  'type': ''}}"
                         ]
                     },
-                    "execution_count": 37,
+                    "execution_count": 5,
                     "metadata": {},
                     "output_type": "execute_result"
                 }
             ],
             "source": [
-                "dst.update([['Inform', 'Cinema', 'area', 'na górze na środku']])\n",
-                "dst.state['belief_state']['cinema']"
+                "dst.update([['Inform', 'Hotel', 'Area', 'north']])\n",
+                "dst.state['belief_state']['hotel']"
             ]
         },
         {
             "cell_type": "code",
-            "execution_count": 38,
+            "execution_count": 6,
             "metadata": {
                 "lines_to_next_cell": 0
             },
@@ -371,45 +349,42 @@
                 {
                     "data": {
                         "text/plain": [
-                            "{'cinema': {'date': 0}}"
+                            "{'hotel': {'area': 0}}"
                         ]
                     },
-                    "execution_count": 38,
+                    "execution_count": 6,
                     "metadata": {},
                     "output_type": "execute_result"
                 }
             ],
             "source": [
-                "dst.update([['Request', 'Cinema', 'date', '?']])\n",
+                "dst.update([['Request', 'Hotel', 'Area', '?']])\n",
                 "dst.state['request_state']"
             ]
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 7,
             "metadata": {
                 "lines_to_next_cell": 0
             },
             "outputs": [
                 {
-                    "ename": "",
-                    "evalue": "",
-                    "output_type": "error",
-                    "traceback": [
-                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
-                        "Run the following command to install 'ipykernel' into the Python environment. \n",
-                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
-                    ]
-                },
-                {
-                    "ename": "",
-                    "evalue": "",
-                    "output_type": "error",
-                    "traceback": [
-                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
-                        "Run the following command to install 'ipykernel' into the Python environment. \n",
-                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
-                    ]
+                    "data": {
+                        "text/plain": [
+                            "{'book': {'booked': [], 'people': '2', 'day': 'tuesday', 'stay': '4'},\n",
+                            " 'semi': {'name': '',\n",
+                            "  'area': 'north',\n",
+                            "  'parking': 'yes',\n",
+                            "  'pricerange': 'cheap',\n",
+                            "  'stars': '',\n",
+                            "  'internet': '',\n",
+                            "  'type': ''}}"
+                        ]
+                    },
+                    "execution_count": 7,
+                    "metadata": {},
+                    "output_type": "execute_result"
                 }
             ],
             "source": [
@@ -419,7 +394,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 39,
+            "execution_count": 8,
             "metadata": {},
             "outputs": [
                 {
@@ -427,20 +402,40 @@
                         "text/plain": [
                             "{'user_action': [],\n",
                             " 'system_action': [],\n",
-                            " 'belief_state': {'cinema': {'book': {'title': 'batman',\n",
-                            "    'date': '',\n",
-                            "    'time': '15:00',\n",
-                            "    'quantity': '',\n",
-                            "    'seats': '',\n",
-                            "    'area': 'na górze na środku',\n",
-                            "    'interval': ''},\n",
-                            "   'semi': {'goal': ''}}},\n",
-                            " 'request_state': {'cinema': {'date': 0}},\n",
+                            " 'belief_state': {'police': {'book': {'booked': []}, 'semi': {}},\n",
+                            "  'hotel': {'book': {'booked': [],\n",
+                            "    'people': '2',\n",
+                            "    'day': 'tuesday',\n",
+                            "    'stay': '4'},\n",
+                            "   'semi': {'name': '',\n",
+                            "    'area': 'north',\n",
+                            "    'parking': 'yes',\n",
+                            "    'pricerange': 'cheap',\n",
+                            "    'stars': '',\n",
+                            "    'internet': '',\n",
+                            "    'type': ''}},\n",
+                            "  'attraction': {'book': {'booked': []},\n",
+                            "   'semi': {'type': '', 'name': '', 'area': ''}},\n",
+                            "  'restaurant': {'book': {'booked': [], 'people': '', 'day': '', 'time': ''},\n",
+                            "   'semi': {'food': '', 'pricerange': '', 'name': '', 'area': ''}},\n",
+                            "  'hospital': {'book': {'booked': []}, 'semi': {'department': ''}},\n",
+                            "  'taxi': {'book': {'booked': []},\n",
+                            "   'semi': {'leaveAt': '',\n",
+                            "    'destination': '',\n",
+                            "    'departure': '',\n",
+                            "    'arriveBy': ''}},\n",
+                            "  'train': {'book': {'booked': [], 'people': ''},\n",
+                            "   'semi': {'leaveAt': '',\n",
+                            "    'destination': '',\n",
+                            "    'day': '',\n",
+                            "    'arriveBy': '',\n",
+                            "    'departure': ''}}},\n",
+                            " 'request_state': {'hotel': {'area': 0}},\n",
                             " 'terminated': False,\n",
                             " 'history': []}"
                         ]
                     },
-                    "execution_count": 39,
+                    "execution_count": 8,
                     "metadata": {},
                     "output_type": "execute_result"
                 }
@@ -471,30 +466,9 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 10,
             "metadata": {},
-            "outputs": [
-                {
-                    "ename": "",
-                    "evalue": "",
-                    "output_type": "error",
-                    "traceback": [
-                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
-                        "Run the following command to install 'ipykernel' into the Python environment. \n",
-                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
-                    ]
-                },
-                {
-                    "ename": "",
-                    "evalue": "",
-                    "output_type": "error",
-                    "traceback": [
-                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
-                        "Run the following command to install 'ipykernel' into the Python environment. \n",
-                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
-                    ]
-                }
-            ],
+            "outputs": [],
             "source": [
                 "from collections import defaultdict\n",
                 "import copy\n",
@@ -575,32 +549,11 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 11,
             "metadata": {
                 "lines_to_next_cell": 0
             },
-            "outputs": [
-                {
-                    "ename": "",
-                    "evalue": "",
-                    "output_type": "error",
-                    "traceback": [
-                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
-                        "Run the following command to install 'ipykernel' into the Python environment. \n",
-                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
-                    ]
-                },
-                {
-                    "ename": "",
-                    "evalue": "",
-                    "output_type": "error",
-                    "traceback": [
-                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
-                        "Run the following command to install 'ipykernel' into the Python environment. \n",
-                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
-                    ]
-                }
-            ],
+            "outputs": [],
             "source": [
                 "from convlab2.dialog_agent import PipelineAgent\n",
                 "dst.init_session()\n",
@@ -610,30 +563,21 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 12,
             "metadata": {
                 "lines_to_next_cell": 0
             },
             "outputs": [
                 {
-                    "ename": "",
-                    "evalue": "",
-                    "output_type": "error",
-                    "traceback": [
-                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
-                        "Run the following command to install 'ipykernel' into the Python environment. \n",
-                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
-                    ]
-                },
-                {
-                    "ename": "",
-                    "evalue": "",
-                    "output_type": "error",
-                    "traceback": [
-                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
-                        "Run the following command to install 'ipykernel' into the Python environment. \n",
-                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
-                    ]
+                    "data": {
+                        "text/plain": [
+                            "[['Inform', 'Hotel', 'Choice', '10'],\n",
+                            " ['Recommend', 'Hotel', 'Name', 'alexander bed and breakfast']]"
+                        ]
+                    },
+                    "execution_count": 12,
+                    "metadata": {},
+                    "output_type": "execute_result"
                 }
             ],
             "source": [
@@ -642,30 +586,21 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 13,
             "metadata": {
                 "lines_to_next_cell": 0
             },
             "outputs": [
                 {
-                    "ename": "",
-                    "evalue": "",
-                    "output_type": "error",
-                    "traceback": [
-                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
-                        "Run the following command to install 'ipykernel' into the Python environment. \n",
-                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
-                    ]
-                },
-                {
-                    "ename": "",
-                    "evalue": "",
-                    "output_type": "error",
-                    "traceback": [
-                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
-                        "Run the following command to install 'ipykernel' into the Python environment. \n",
-                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
-                    ]
+                    "data": {
+                        "text/plain": [
+                            "[['Inform', 'Hotel', 'Choice', '2'],\n",
+                            " ['Recommend', 'Hotel', 'Name', 'city centre north b and b']]"
+                        ]
+                    },
+                    "execution_count": 13,
+                    "metadata": {},
+                    "output_type": "execute_result"
                 }
             ],
             "source": [
@@ -674,30 +609,20 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 14,
             "metadata": {
                 "lines_to_next_cell": 0
             },
             "outputs": [
                 {
-                    "ename": "",
-                    "evalue": "",
-                    "output_type": "error",
-                    "traceback": [
-                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
-                        "Run the following command to install 'ipykernel' into the Python environment. \n",
-                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
-                    ]
-                },
-                {
-                    "ename": "",
-                    "evalue": "",
-                    "output_type": "error",
-                    "traceback": [
-                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
-                        "Run the following command to install 'ipykernel' into the Python environment. \n",
-                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
-                    ]
+                    "data": {
+                        "text/plain": [
+                            "[['Inform', 'Hotel', 'Area', 'north']]"
+                        ]
+                    },
+                    "execution_count": 14,
+                    "metadata": {},
+                    "output_type": "execute_result"
                 }
             ],
             "source": [
@@ -706,28 +631,18 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 15,
             "metadata": {},
             "outputs": [
                 {
-                    "ename": "",
-                    "evalue": "",
-                    "output_type": "error",
-                    "traceback": [
-                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
-                        "Run the following command to install 'ipykernel' into the Python environment. \n",
-                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
-                    ]
-                },
-                {
-                    "ename": "",
-                    "evalue": "",
-                    "output_type": "error",
-                    "traceback": [
-                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
-                        "Run the following command to install 'ipykernel' into the Python environment. \n",
-                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
-                    ]
+                    "data": {
+                        "text/plain": [
+                            "[['Book', 'Booking', 'Ref', '00000013']]"
+                        ]
+                    },
+                    "execution_count": 15,
+                    "metadata": {},
+                    "output_type": "execute_result"
                 }
             ],
             "source": [
@@ -746,27 +661,16 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 20,
             "metadata": {},
             "outputs": [
                 {
-                    "ename": "",
-                    "evalue": "",
-                    "output_type": "error",
-                    "traceback": [
-                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
-                        "Run the following command to install 'ipykernel' into the Python environment. \n",
-                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
-                    ]
-                },
-                {
-                    "ename": "",
-                    "evalue": "",
-                    "output_type": "error",
-                    "traceback": [
-                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
-                        "Run the following command to install 'ipykernel' into the Python environment. \n",
-                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "[<convlab2.nlu.svm.Features.nbest object at 0x0000028BDD2032C8>]\n",
+                        "loading saved Classifier\n",
+                        "loaded.\n"
                     ]
                 }
             ],
@@ -781,29 +685,27 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 17,
             "metadata": {
                 "lines_to_next_cell": 0
             },
             "outputs": [
                 {
-                    "ename": "",
-                    "evalue": "",
+                    "ename": "AttributeError",
+                    "evalue": "'SVC' object has no attribute '_impl'",
                     "output_type": "error",
                     "traceback": [
-                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
-                        "Run the following command to install 'ipykernel' into the Python environment. \n",
-                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
-                    ]
-                },
-                {
-                    "ename": "",
-                    "evalue": "",
-                    "output_type": "error",
-                    "traceback": [
-                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
-                        "Run the following command to install 'ipykernel' into the Python environment. \n",
-                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
+                        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+                        "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+                        "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_13500\\3260802613.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0magent\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresponse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"I need a cheap hotel with free parking .\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+                        "\u001b[1;32mc:\\develop\\wmi\\aitech\\sem1\\systemy dialogowe\\lab\\convlab-2\\convlab2\\dialog_agent\\agent.py\u001b[0m in \u001b[0;36mresponse\u001b[1;34m(self, observation)\u001b[0m\n\u001b[0;32m    120\u001b[0m         \u001b[1;31m# get dialog act\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    121\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlu\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 122\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minput_action\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlu\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobservation\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    123\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    124\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minput_action\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mobservation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+                        "\u001b[1;32mc:\\develop\\wmi\\aitech\\sem1\\systemy dialogowe\\lab\\convlab-2\\convlab2\\nlu\\svm\\multiwoz\\nlu.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, utterance, context)\u001b[0m\n\u001b[0;32m     74\u001b[0m                 ]\n\u001b[0;32m     75\u001b[0m             }\n\u001b[1;32m---> 76\u001b[1;33m         \u001b[0mslu_hyps\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdecode_sent\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msentinfo\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"decode\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"output\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     77\u001b[0m         \u001b[0mact_list\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     78\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mhyp\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mslu_hyps\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+                        "\u001b[1;32mc:\\develop\\wmi\\aitech\\sem1\\systemy dialogowe\\lab\\convlab-2\\convlab2\\nlu\\svm\\Classifier.py\u001b[0m in \u001b[0;36mdecode_sent\u001b[1;34m(self, sentinfo, output_fname, config)\u001b[0m\n\u001b[0;32m    320\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    321\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextractFeatures2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msentinfo\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlog_input_key\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlog_input_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 322\u001b[1;33m         \u001b[0mdecode_results\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    323\u001b[0m         \u001b[0mcounter\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdefaultdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    324\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+                        "\u001b[1;32mc:\\develop\\wmi\\aitech\\sem1\\systemy dialogowe\\lab\\convlab-2\\convlab2\\nlu\\svm\\Classifier.py\u001b[0m in \u001b[0;36mdecode\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    258\u001b[0m             \u001b[0mbaseXs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbaseXs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mindex\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbaseX_pointers\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mthis_tuple\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    259\u001b[0m             \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtoSparse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbaseXs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mthis_tuple\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdictionary\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 260\u001b[1;33m             \u001b[0mresults\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mthis_tuple\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclassifiers\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mthis_tuple\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    261\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    262\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+                        "\u001b[1;32mc:\\develop\\wmi\\aitech\\sem1\\systemy dialogowe\\lab\\convlab-2\\convlab2\\nlu\\svm\\Classifier.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    496\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    497\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 498\u001b[1;33m         \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict_proba\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    499\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    500\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+                        "\u001b[1;32mc:\\Develop\\wmi\\AITECH\\sem1\\Systemy dialogowe\\lab\\venv\\lib\\site-packages\\sklearn\\svm\\base.py\u001b[0m in \u001b[0;36mpredict_proba\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    588\u001b[0m         \u001b[0mdatasets\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    589\u001b[0m         \"\"\"\n\u001b[1;32m--> 590\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_check_proba\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    591\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_predict_proba\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    592\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+                        "\u001b[1;32mc:\\Develop\\wmi\\AITECH\\sem1\\Systemy dialogowe\\lab\\venv\\lib\\site-packages\\sklearn\\svm\\base.py\u001b[0m in \u001b[0;36m_check_proba\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    557\u001b[0m             raise AttributeError(\"predict_proba is not available when \"\n\u001b[0;32m    558\u001b[0m                                  \" probability=False\")\n\u001b[1;32m--> 559\u001b[1;33m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_impl\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m'c_svc'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'nu_svc'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    560\u001b[0m             raise AttributeError(\"predict_proba only implemented for SVC\"\n\u001b[0;32m    561\u001b[0m                                  \" and NuSVC\")\n",
+                        "\u001b[1;31mAttributeError\u001b[0m: 'SVC' object has no attribute '_impl'"
                     ]
                 }
             ],
@@ -813,29 +715,27 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 18,
             "metadata": {
                 "lines_to_next_cell": 0
             },
             "outputs": [
                 {
-                    "ename": "",
-                    "evalue": "",
+                    "ename": "AttributeError",
+                    "evalue": "'SVC' object has no attribute '_impl'",
                     "output_type": "error",
                     "traceback": [
-                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
-                        "Run the following command to install 'ipykernel' into the Python environment. \n",
-                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
-                    ]
-                },
-                {
-                    "ename": "",
-                    "evalue": "",
-                    "output_type": "error",
-                    "traceback": [
-                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
-                        "Run the following command to install 'ipykernel' into the Python environment. \n",
-                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
+                        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+                        "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+                        "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_13500\\2723043776.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0magent\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresponse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Where it is located ?\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+                        "\u001b[1;32mc:\\develop\\wmi\\aitech\\sem1\\systemy dialogowe\\lab\\convlab-2\\convlab2\\dialog_agent\\agent.py\u001b[0m in \u001b[0;36mresponse\u001b[1;34m(self, observation)\u001b[0m\n\u001b[0;32m    120\u001b[0m         \u001b[1;31m# get dialog act\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    121\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlu\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 122\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minput_action\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlu\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobservation\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    123\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    124\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minput_action\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mobservation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+                        "\u001b[1;32mc:\\develop\\wmi\\aitech\\sem1\\systemy dialogowe\\lab\\convlab-2\\convlab2\\nlu\\svm\\multiwoz\\nlu.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, utterance, context)\u001b[0m\n\u001b[0;32m     74\u001b[0m                 ]\n\u001b[0;32m     75\u001b[0m             }\n\u001b[1;32m---> 76\u001b[1;33m         \u001b[0mslu_hyps\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdecode_sent\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msentinfo\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"decode\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"output\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     77\u001b[0m         \u001b[0mact_list\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     78\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mhyp\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mslu_hyps\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+                        "\u001b[1;32mc:\\develop\\wmi\\aitech\\sem1\\systemy dialogowe\\lab\\convlab-2\\convlab2\\nlu\\svm\\Classifier.py\u001b[0m in \u001b[0;36mdecode_sent\u001b[1;34m(self, sentinfo, output_fname, config)\u001b[0m\n\u001b[0;32m    320\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    321\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextractFeatures2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msentinfo\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlog_input_key\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlog_input_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 322\u001b[1;33m         \u001b[0mdecode_results\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    323\u001b[0m         \u001b[0mcounter\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdefaultdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    324\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+                        "\u001b[1;32mc:\\develop\\wmi\\aitech\\sem1\\systemy dialogowe\\lab\\convlab-2\\convlab2\\nlu\\svm\\Classifier.py\u001b[0m in \u001b[0;36mdecode\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    258\u001b[0m             \u001b[0mbaseXs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbaseXs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mindex\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbaseX_pointers\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mthis_tuple\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    259\u001b[0m             \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtoSparse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbaseXs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mthis_tuple\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdictionary\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 260\u001b[1;33m             \u001b[0mresults\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mthis_tuple\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclassifiers\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mthis_tuple\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    261\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    262\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+                        "\u001b[1;32mc:\\develop\\wmi\\aitech\\sem1\\systemy dialogowe\\lab\\convlab-2\\convlab2\\nlu\\svm\\Classifier.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    496\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    497\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 498\u001b[1;33m         \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict_proba\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    499\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    500\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+                        "\u001b[1;32mc:\\Develop\\wmi\\AITECH\\sem1\\Systemy dialogowe\\lab\\venv\\lib\\site-packages\\sklearn\\svm\\base.py\u001b[0m in \u001b[0;36mpredict_proba\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    588\u001b[0m         \u001b[0mdatasets\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    589\u001b[0m         \"\"\"\n\u001b[1;32m--> 590\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_check_proba\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    591\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_predict_proba\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    592\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+                        "\u001b[1;32mc:\\Develop\\wmi\\AITECH\\sem1\\Systemy dialogowe\\lab\\venv\\lib\\site-packages\\sklearn\\svm\\base.py\u001b[0m in \u001b[0;36m_check_proba\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    557\u001b[0m             raise AttributeError(\"predict_proba is not available when \"\n\u001b[0;32m    558\u001b[0m                                  \" probability=False\")\n\u001b[1;32m--> 559\u001b[1;33m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_impl\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m'c_svc'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'nu_svc'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    560\u001b[0m             raise AttributeError(\"predict_proba only implemented for SVC\"\n\u001b[0;32m    561\u001b[0m                                  \" and NuSVC\")\n",
+                        "\u001b[1;31mAttributeError\u001b[0m: 'SVC' object has no attribute '_impl'"
                     ]
                 }
             ],
@@ -845,29 +745,27 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 19,
             "metadata": {
                 "lines_to_next_cell": 0
             },
             "outputs": [
                 {
-                    "ename": "",
-                    "evalue": "",
+                    "ename": "AttributeError",
+                    "evalue": "'SVC' object has no attribute '_impl'",
                     "output_type": "error",
                     "traceback": [
-                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
-                        "Run the following command to install 'ipykernel' into the Python environment. \n",
-                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
-                    ]
-                },
-                {
-                    "ename": "",
-                    "evalue": "",
-                    "output_type": "error",
-                    "traceback": [
-                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
-                        "Run the following command to install 'ipykernel' into the Python environment. \n",
-                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
+                        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+                        "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+                        "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_13500\\871593950.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0magent\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresponse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"I would prefer the hotel be in the north part of town .\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+                        "\u001b[1;32mc:\\develop\\wmi\\aitech\\sem1\\systemy dialogowe\\lab\\convlab-2\\convlab2\\dialog_agent\\agent.py\u001b[0m in \u001b[0;36mresponse\u001b[1;34m(self, observation)\u001b[0m\n\u001b[0;32m    120\u001b[0m         \u001b[1;31m# get dialog act\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    121\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlu\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 122\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minput_action\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlu\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobservation\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    123\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    124\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minput_action\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mobservation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+                        "\u001b[1;32mc:\\develop\\wmi\\aitech\\sem1\\systemy dialogowe\\lab\\convlab-2\\convlab2\\nlu\\svm\\multiwoz\\nlu.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, utterance, context)\u001b[0m\n\u001b[0;32m     74\u001b[0m                 ]\n\u001b[0;32m     75\u001b[0m             }\n\u001b[1;32m---> 76\u001b[1;33m         \u001b[0mslu_hyps\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdecode_sent\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msentinfo\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"decode\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"output\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     77\u001b[0m         \u001b[0mact_list\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     78\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mhyp\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mslu_hyps\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+                        "\u001b[1;32mc:\\develop\\wmi\\aitech\\sem1\\systemy dialogowe\\lab\\convlab-2\\convlab2\\nlu\\svm\\Classifier.py\u001b[0m in \u001b[0;36mdecode_sent\u001b[1;34m(self, sentinfo, output_fname, config)\u001b[0m\n\u001b[0;32m    320\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    321\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextractFeatures2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msentinfo\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlog_input_key\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlog_input_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 322\u001b[1;33m         \u001b[0mdecode_results\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    323\u001b[0m         \u001b[0mcounter\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdefaultdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    324\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+                        "\u001b[1;32mc:\\develop\\wmi\\aitech\\sem1\\systemy dialogowe\\lab\\convlab-2\\convlab2\\nlu\\svm\\Classifier.py\u001b[0m in \u001b[0;36mdecode\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    258\u001b[0m             \u001b[0mbaseXs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbaseXs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mindex\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbaseX_pointers\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mthis_tuple\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    259\u001b[0m             \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtoSparse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbaseXs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mthis_tuple\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdictionary\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 260\u001b[1;33m             \u001b[0mresults\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mthis_tuple\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclassifiers\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mthis_tuple\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    261\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    262\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+                        "\u001b[1;32mc:\\develop\\wmi\\aitech\\sem1\\systemy dialogowe\\lab\\convlab-2\\convlab2\\nlu\\svm\\Classifier.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    496\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    497\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 498\u001b[1;33m         \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict_proba\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    499\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    500\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+                        "\u001b[1;32mc:\\Develop\\wmi\\AITECH\\sem1\\Systemy dialogowe\\lab\\venv\\lib\\site-packages\\sklearn\\svm\\base.py\u001b[0m in \u001b[0;36mpredict_proba\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    588\u001b[0m         \u001b[0mdatasets\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    589\u001b[0m         \"\"\"\n\u001b[1;32m--> 590\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_check_proba\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    591\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_predict_proba\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    592\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+                        "\u001b[1;32mc:\\Develop\\wmi\\AITECH\\sem1\\Systemy dialogowe\\lab\\venv\\lib\\site-packages\\sklearn\\svm\\base.py\u001b[0m in \u001b[0;36m_check_proba\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    557\u001b[0m             raise AttributeError(\"predict_proba is not available when \"\n\u001b[0;32m    558\u001b[0m                                  \" probability=False\")\n\u001b[1;32m--> 559\u001b[1;33m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_impl\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m'c_svc'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'nu_svc'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    560\u001b[0m             raise AttributeError(\"predict_proba only implemented for SVC\"\n\u001b[0;32m    561\u001b[0m                                  \" and NuSVC\")\n",
+                        "\u001b[1;31mAttributeError\u001b[0m: 'SVC' object has no attribute '_impl'"
                     ]
                 }
             ],
@@ -879,28 +777,7 @@
             "cell_type": "code",
             "execution_count": null,
             "metadata": {},
-            "outputs": [
-                {
-                    "ename": "",
-                    "evalue": "",
-                    "output_type": "error",
-                    "traceback": [
-                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
-                        "Run the following command to install 'ipykernel' into the Python environment. \n",
-                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
-                    ]
-                },
-                {
-                    "ename": "",
-                    "evalue": "",
-                    "output_type": "error",
-                    "traceback": [
-                        "\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
-                        "Run the following command to install 'ipykernel' into the Python environment. \n",
-                        "Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
-                    ]
-                }
-            ],
+            "outputs": [],
             "source": [
                 "agent.response(\"Yeah , could you book me a room for 2 people for 4 nights starting Tuesday ?\")"
             ]
diff --git a/src/components/DP.py b/src/components/DP.py
index 7f5feed..927e69d 100644
--- a/src/components/DP.py
+++ b/src/components/DP.py
@@ -1,11 +1,31 @@
-# Martyna
+from urllib import request
+
+
 class DP:
     
-    def __init__(self, slots):
-        self.slots = slots
+    def __init__(self):
+        pass
 
-    def getAct(slots):
-        # iterate over slots
-        # find empty
-        # returns system act
-        pass
\ No newline at end of file
+    def getAction(self, lastUserAct, emptySlots, systemSlots):
+        systemAct = None
+        slotVal = None
+        if ((lastUserAct == "hello") | (lastUserAct == "inform") | (lastUserAct == None)):
+            # there are no empty slots
+            if not emptySlots:
+                systemAct = "inform"
+                slotVal = systemSlots
+            # there are empty slots
+            else: 
+                for slot in systemSlots:
+                    if slot in emptySlots:
+                        systemAct = "request"
+                        slotVal = slot
+                        break
+            return ["Cinema", systemAct, slotVal, ""]
+        elif (lastUserAct == "request"):
+            # todo policy for user request
+            return ["Cinema", "", "", ""]
+        else:
+            systemAct = "repeat"
+            return ["Cinema", systemAct, "", ""]
+ 
\ No newline at end of file
diff --git a/src/components/DST.py b/src/components/DST.py
index cb23947..1699122 100644
--- a/src/components/DST.py
+++ b/src/components/DST.py
@@ -5,10 +5,13 @@ class DST:
         # self.value_dict = json.load(open('utils/value_dict.json'))
 
     def update(self, user_act=None):
+        intentVal = None
         for intent, domain, slot, value in user_act:
             domain = domain.lower()
             intent = intent.lower()
             value = value.lower()
+            
+            if intentVal is None : intentVal = intent
 
             k = slot
 
@@ -22,15 +25,38 @@ class DST:
                     self.state['belief_state'][domain]['book'][k] = value
                 elif k.lower() in domain_dic['book']:
                     self.state['belief_state'][domain]['book'][k.lower()] = value
-            elif intent == 'request':
+            elif intent == 'reqmore':
 
                 if domain not in self.state['request_state']:
                     self.state['request_state'][domain] = {}
                 if k not in self.state['request_state'][domain]:
                     self.state['request_state'][domain][k] = 0
-
+        self.state['user_action'].append(intentVal)
         return self.state
 
+    def addSystemAct(self, act):
+        self.state["system_action"].append(act)
+
+    def getLastUserAct(self):
+        try:
+            return self.state["user_action"][-1]
+        except:
+            return None
+
+    def getEmptySlots(self):
+        result = []
+        for key in self.state['belief_state']["cinema"]["book"].keys():
+            if self.state['belief_state']["cinema"]["book"][key] == "":
+                result.append(key)
+        return result
+
+    def getSystemSlots(self):
+        result = []
+        for key in self.state['belief_state']["cinema"]["book"].keys():
+            if key != "interval":
+                result.append(key)
+        return result
+
     def init_session(self):
         self.state = self.default_state()
 
diff --git a/src/dialogue_system.py b/src/dialogue_system.py
index 9de65ca..41ca86e 100644
--- a/src/dialogue_system.py
+++ b/src/dialogue_system.py
@@ -1,6 +1,7 @@
 from components.NLU import NLU
 from components.NLG import NLG
 from components.DST import DST
+from components.DP import DP
 
 def chatbot():
 
@@ -9,6 +10,7 @@ def chatbot():
     # NLU
     nlu = NLU()
     dst = DST()
+    dp = DP()
 
     # hello message
     print("wpisz /exit aby zakończyć")
@@ -22,6 +24,10 @@ def chatbot():
             isActive = False
         else:
             nluPred = nlu.predict(sentence=userMessage)
+            # print(nluPred)
             dst.update(nluPred)
-            print(dst.state)
+            # print(dst.state)
+            dpAct = dp.getAction(dst.getLastUserAct(), dst.getEmptySlots(), dst.getSystemSlots())
+            print(dpAct)
+            # todo update DST system act
 chatbot()
\ No newline at end of file