Resolved conflicts

Chipe1 · Chipe1 · commit e7479db97275 · 2016-03-12T04:48:54.000+05:30
diff --git a/search.py b/search.py
@@ -43,9 +43,12 @@ def result(self, state, action):
 
     def goal_test(self, state):
         """Return True if the state is a goal. The default method compares the
-        state to self.goal, as specified in the constructor. Override this
+        state to self.goal or checks for state in self.goal if it is a list, as specified in the constructor. Override this
         method if checking against a single self.goal is not enough."""
-        return state == self.goal
+        if isinstance(self.goal, list):
+            return is_in(state, self.goal)
+        else:
+            return state == self.goal
 
     def path_cost(self, c, state1, action, state2):
         """Return the cost of a solution path that arrives at state2 from
@@ -382,10 +385,10 @@ def and_or_graph_search(problem):
     # functions used by and_or_search
     def or_search(state, problem, path):
         if problem.goal_test(state):
-            return {}
+            return []
         if state in path:
             return None
-        for action in problem.action(state):
+        for action in problem.actions(state):
             plan = and_search(problem.result(state, action),
                               problem, path + [state, ])
             if plan is not None:
@@ -616,6 +619,29 @@ def distance_to_node(n):
     Sibiu=(207, 457), Timisoara=(94, 410), Urziceni=(456, 350),
     Vaslui=(509, 444), Zerind=(108, 531))
 
+"""
+Eight possible states of the vacumm world
+Each state is represented as "State if the left room" "State of the right room" "Room in which the agent is present"
+1 Dirty Dirty Left - DDL
+2 Dirty Dirty Right - DDR
+3 Dirty Clean Left - DCL
+4 Dirty Clean Right - DCR
+5 Clean Dirty Left - CDL
+6 Clean Dirty Right - CDR
+7 Clean Clean Left - CCL
+8 Clean Clean Right - CCR
+"""
+Fig[4, 9] = Graph(dict(
+    State_1 = dict(Suck = ['State_7', 'State_5'], Right = ['State_2']),
+    State_2 = dict(Suck = ['State_8', 'State_4'], Left = ['State_2']),
+    State_3 = dict(Suck = ['State_7'], Right = ['State_4']),
+    State_4 = dict(Suck = ['State_4', 'State_2'], Left = ['State_3']),
+    State_5 = dict(Suck = ['State_5', 'State_1'], Right = ['State_6']),
+    State_6 = dict(Suck = ['State_8'], Left = ['State_5']),
+    State_7 = dict(Suck = ['State_7', 'State_3'], Right = ['State_8']),
+    State_8 = dict(Suck = ['State_8', 'State_6'], Left = ['State_7'])
+))
+
 # Principal states and territories of Australia
 Fig[6, 1] = UndirectedGraph(dict(
     T=dict(),
@@ -655,6 +681,20 @@ def h(self, node):
         else:
             return infinity
 
+class GraphProblemStochastic(GraphProblem):
+    """
+    A version of Graph Problem where an action can lead to undeterministic output i.e. multiple possible states
+    Define the graph as dict(A = dict(Action = [[<Result 1>, <Result 2>, ...],<cost>], ...), ...)
+    A the dictionary format is different, make sure the graph is created as a directed graph
+    """
+    
+    def result(self, state, action):
+        return self.graph.get(state, action)
+
+    def path_cost():
+        raise NotImplementedError
+
+
 # ______________________________________________________________________________
 
 
diff --git a/tests/test_search.py b/tests/test_search.py
@@ -1,8 +1,10 @@
 import pytest
 from search import *  # noqa
+from random import choice #noqa
 
 
 romania = GraphProblem('Arad', 'Bucharest', Fig[3, 2])
+vacumm_world = GraphProblemStochastic('State_1', ['State_7', 'State_8'], Fig[4, 9])
 
 
 def test_breadth_first_tree_search():
@@ -31,5 +33,17 @@ def test_iterative_deepening_search():
                                                               'Fagaras',
                                                               'Bucharest']
 
+def test_and_or_graph_search():
+    def run_plan(state, problem, plan):
+        if problem.goal_test(state):
+            return True
+        if len(plan) is not 2:
+            return False
+        next_state = choice(problem.result(state, plan[0]))
+        return run_plan(next_state, problem, plan[1][next_state])
+    plan = and_or_graph_search(vacumm_world)
+    assert run_plan('State_1', vacumm_world, plan)
+
+
 if __name__ == '__main__':
     pytest.main()
