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master_2.0
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9
.vscode/settings.json
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{
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"python.analysis.extraPaths": [
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"./sources",
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"/usr/local/lib/python3.10/site-packages",
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"/Users/alexeybrown/development",
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"/Users/alexeybrown/development/flutter/",
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"./agent"
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]
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}
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10
README.md
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# si23traktor
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Projekt zaliczeniowy przedmiotu Sztuczna Inteligencja w semestrze letnim 2023.
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Skład zespołu:
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- Jakub Chmielecki
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- Mikołaj Mazur
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- Szymon Szczubkowski
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- Tobiasz Przybylski
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- Aliaksei Shauchenka
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agent/.DS_Store
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agent/methods/.DS_Store
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agent/methods/__pycache__/graph_search.cpython-310.pyc
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agent/methods/graph_search.py
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class Node:
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def __init__(self, state, parent='', action='', distance=0):
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self.state = state
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self.parent = parent
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self.action = action
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self.distance = distance
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class Search:
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def __init__(self, cell_size, cell_number):
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self.cell_size = cell_size
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self.cell_number = cell_number
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def succ(self, state):
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x = state[0]
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y = state[1]
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angle = state[2]
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match(angle):
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case 'UP':
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possible = [['left', x, y, 'LEFT'], ['right', x, y, 'RIGHT']]
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if y != 0: possible.append(['move', x, y - 1, 'UP'])
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return possible
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case 'RIGHT':
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possible = [['left', x, y, 'UP'], ['right', x, y, 'DOWN']]
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if x != (self.cell_number-1): possible.append(['move', x + 1, y, 'RIGHT'])
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return possible
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case 'DOWN':
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possible = [['left', x, y, 'RIGHT'], ['right', x, y, 'LEFT']]
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if y != (self.cell_number-1): possible.append(['move', x, y + 1, 'DOWN'])
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return possible
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case 'LEFT':
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possible = [['left', x, y, 'DOWN'], ['right', x, y, 'UP']]
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if x != 0: possible.append(['move', x - 1, y, 'LEFT'])
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return possible
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def cost(self, node, stones, goal, flowers):
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# cost = node.distance
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cost = 0
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# cost += 10 if stones[node.state[0], node.state[1]] == 1 else 1
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cost += 1000 if (node.state[0], node.state[1]) in stones else 1
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cost += 10 if ((node.state[0]), (node.state[1])) in flowers else 1
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if node.parent:
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node = node.parent
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cost += node.distance # should return only elem.action in prod
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return cost
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def heuristic(self, node, goal):
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return abs(node.state[0] - goal[0]) + abs(node.state[1] - goal[1])
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#bandaid to know about stones
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def astarsearch(self, istate, goaltest, stone_list, plant_list):
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#to be expanded
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def cost_old(x, y):
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if (x, y) in stones:
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return 10
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else:
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return 1
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x = istate[0]
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y = istate[1]
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angle = istate[2]
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stones = []
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flowers = []
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for obj in stone_list:
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stones.append((obj.xy[0]*50, obj.xy[1]*50))
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for obj in plant_list:
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if obj.name == 'flower':
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flowers.append((obj.xy[0]*50, obj.xy[1]*50))
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# stones = [(x*50, y*50) for (x, y) in stone_list]
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# flowers = [(x*50, y*50) for (x, y) in plant_list]
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print(stones)
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# fringe = [(Node([x, y, angle]), cost_old(x, y))] # queue (moves/states to check)
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fringe = [(Node([x, y, angle]))] # queue (moves/states to check)
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fringe[0].distance = self.cost(fringe[0], stones, goaltest, flowers)
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fringe.append((Node([x, y, angle]), self.cost(fringe[0], stones, goaltest, flowers)))
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fringe.pop(0)
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explored = []
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while True:
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if len(fringe) == 0:
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return False
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fringe.sort(key=lambda x: x[1])
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elem = fringe.pop(0)[0]
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# if goal_test(elem.state):
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# return
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# print(elem.state[0], elem.state[1], elem.state[2])
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if elem.state[0] == goaltest[0] and elem.state[1] == goaltest[1]: # checks if we reached the given point
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steps = []
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while elem.parent:
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steps.append([elem.action, elem.state[0], elem.state[1]]) # should return only elem.action in prod
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elem = elem.parent
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steps.reverse()
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print(steps) # only for dev
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return steps
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explored.append(elem.state)
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for (action, state_x, state_y, state_angle) in self.succ(elem.state):
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x = Node([state_x, state_y, state_angle], elem, action)
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x.parent = elem
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priority = self.cost(elem, stones, goaltest, flowers) + self.heuristic(elem, goaltest)
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elem.distance = priority
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# priority = cost_old(x, y) + self.heuristic(elem, goaltest)
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fringe_states = [node.state for (node, p) in fringe]
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if x.state not in fringe_states and x.state not in explored:
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fringe.append((x, priority))
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elif x.state in fringe_states:
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for i in range(len(fringe)):
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if fringe[i][0].state == x.state:
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if fringe[i][1] > priority:
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fringe[i] = (x, priority)
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def closest_point(self, x, y, name, plant_list):
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self.max_distance = self.cell_number*self.cell_number
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for obj in plant_list:
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if obj.name == name:
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if obj.state == 0:
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self.distance = (abs(obj.xy[0] - x) + abs(obj.xy[1] - y))
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if self.distance <= self.max_distance:
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self.max_distance = self.distance
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x_close = obj.xy[0]
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y_close = obj.xy[1]
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#print("distance: ",self.distance, obj.xy[0], "+", obj.xy[1], "-" ,x, "+",y)
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return (x_close, y_close)
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agent/neural_network/.DS_Store
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agent/neural_network/__pycache__/inference.cpython-310.pyc
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agent/neural_network/__pycache__/model.cpython-310.pyc
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agent/neural_network/images/.DS_Store
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