First commit.

agent.py

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+#!/usr/bin/env python3
+
+from environment import State, goalTest, actions, result
+from collections import deque
+
+# Example of goal-oriented agent.
+
+frontier = deque() # Initialize the frontier as a queue
+state = State() # Generate the initial state
+while not goalTest(state): # Apply the global test
+    for action in actions(state): # Expand the node
+        frontier.append(result(state, action))
+    state = frontier.popleft()
+state.printState()

environment.py

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+#!/usr/bin/env python3
+
+from random import shuffle
+from copy import deepcopy
+
+# Example of environment, for the game of fifteen.
+
+# Maximum rows and columns size
+MAX_SIZE = 4
+
+class State:
+    """Represents a state of the environment"""
+
+    def __init__(self):
+        elements = [x for x in range(MAX_SIZE**2)]
+        shuffle(elements)
+        self.matrix = [[elements[x+(y*MAX_SIZE)] for x in range(MAX_SIZE)] for y in range(MAX_SIZE)]
+    def printState(self):
+        for i in range(MAX_SIZE):
+            print(self.matrix[i])
+        print("\n")
+
+def up(state):
+    (x, y) = findZero(state)
+    (state.matrix[y][x], state.matrix[y-1][x]) = (state.matrix[y-1][x], state.matrix[y][x])
+
+def down(state):
+    (x, y) = findZero(state)
+    (state.matrix[y][x], state.matrix[y+1][x]) = (state.matrix[y+1][x], state.matrix[y][x])
+
+def left(state):
+    (x, y) = findZero(state)
+    (state.matrix[y][x], state.matrix[y][x-1]) = (state.matrix[y][x-1], state.matrix[y][x])
+
+def right(state):
+    (x, y) = findZero(state)
+    (state.matrix[y][x], state.matrix[y][x+1]) = (state.matrix[y][x+1], state.matrix[y][x])
+
+def findZero(state):
+    for row in state.matrix:
+        try:
+            x = row.index(0)
+            y = state.matrix.index(row)
+            break
+        except ValueError:
+            pass
+    return (x, y)
+
+def actions(state):
+    (x, y) = findZero(state)
+    actions = []
+    if x > 0:
+        actions.append(left)
+    if x < MAX_SIZE-1:
+        actions.append(right)
+    if y > 0:
+        actions.append(up)
+    if y < MAX_SIZE-1:
+        actions.append(down)
+    return actions
+
+def result(state, action):
+    newState = deepcopy(state)
+    action(newState)
+    return newState
+
+def goalTest(state):
+    el = []
+    for row in state.matrix:
+        el.extend(row)
+    return all(el[i] < el[i+1] for i in range(len(el)-1))
+
+if __name__ == "__main__":
+    s0 = State()
+    for action in actions(s0):
+        result(s0, action).printState()