Added critical heuristic

Author: sampreets3

example-run.txt

@@ -0,0 +1,42 @@
+A* - Delete Relaxation - H_Max
+        ======PLAN (Nodes)=======
+['Node { object(NewYork), object(Pittsburgh), object(Toronto), object(Boston), object(Albany), connected(Pittsburgh, Boston), connected(Toronto, Albany), visited(Boston), connected(NewYork, Albany), connected(NewYork, Toronto), connected(Toronto, Pittsburgh), connected(NewYork, Boston), connected(Boston, Pittsburgh), connected(Albany, NewYork), connected(NewYork, Pittsburgh), connected(Albany, Toronto), connected(Boston, NewYork), connected(Pittsburgh, NewYork), at(Boston), connected(Toronto, NewYork), total-cost = 3 | g = 3.00 | h = inf | open = False | closed = True }', 'Node { object(NewYork), object(Pittsburgh), object(Toronto), object(Boston), object(Albany), connected(Pittsburgh, Boston), connected(Toronto, Albany), visited(Boston), connected(NewYork, Albany), connected(NewYork, Toronto), connected(Toronto, Pittsburgh), connected(NewYork, Boston), connected(Boston, Pittsburgh), connected(Albany, NewYork), connected(NewYork, Pittsburgh), connected(Albany, Toronto), connected(Boston, NewYork), connected(Pittsburgh, NewYork), connected(Toronto, NewYork), at(NewYork), visited(NewYork), total-cost = 6 | g = 6.00 | h = inf | open = False | closed = True }', 'Node { object(NewYork), object(Pittsburgh), object(Toronto), object(Boston), object(Albany), connected(Pittsburgh, Boston), connected(Toronto, Albany), visited(Boston), connected(NewYork, Albany), connected(NewYork, Toronto), connected(Toronto, Pittsburgh), at(Albany), connected(NewYork, Boston), connected(Boston, Pittsburgh), connected(Albany, NewYork), visited(Albany), connected(NewYork, Pittsburgh), connected(Albany, Toronto), connected(Boston, NewYork), connected(Pittsburgh, NewYork), connected(Toronto, NewYork), visited(NewYork), total-cost = 9 | g = 9.00 | h = inf | open = False | closed = True }', 'Node { object(NewYork), object(Pittsburgh), object(Toronto), object(Boston), object(Albany), at(Toronto), connected(Pittsburgh, Boston), connected(Toronto, Albany), visited(Boston), connected(NewYork, Albany), connected(NewYork, Toronto), connected(Toronto, Pittsburgh), connected(NewYork, Boston), connected(Boston, Pittsburgh), visited(Toronto), connected(Albany, NewYork), visited(Albany), connected(NewYork, Pittsburgh), connected(Albany, Toronto), connected(Boston, NewYork), connected(Pittsburgh, NewYork), connected(Toronto, NewYork), visited(NewYork), total-cost = 12 | g = 12.00 | h = inf | open = False | closed = True }', 'Node { object(NewYork), object(Pittsburgh), object(Toronto), object(Boston), object(Albany), connected(Pittsburgh, Boston), connected(Toronto, Albany), visited(Boston), connected(NewYork, Albany), connected(NewYork, Toronto), connected(Toronto, Pittsburgh), connected(NewYork, Boston), visited(Pittsburgh), connected(Boston, Pittsburgh), at(Pittsburgh), visited(Toronto), connected(Albany, NewYork), visited(Albany), connected(NewYork, Pittsburgh), connected(Albany, Toronto), connected(Boston, NewYork), connected(Pittsburgh, NewYork), connected(Toronto, NewYork), visited(NewYork), total-cost = 15 | g = 15.00 | h = 15.00 | open = True | closed = False }']
+        ======PLAN (Actions)=======
+('move(Pittsburgh, Boston)>', 'total-cost = 3')
+('move(Boston, NewYork)>', 'total-cost = 6')
+('move(NewYork, Albany)>', 'total-cost = 9')
+('move(Albany, Toronto)>', 'total-cost = 12')
+('move(Toronto, Pittsburgh)>', 'total-cost = 15')
+
+        ======METRICS=======
+Expanded 17 state(s).
+Opened 27 state(s).
+Reopened 0 state(s).
+Evaluated 21 state(s).
+Generated 28 state(s).
+Dead ends: 3 state(s).
+Runtime: 1.46s.
+Total heuristic runtime: 2.37s
+Computational weight of heuristic in the search: 162.00%
+
+        A* - Delete Relaxation - H_Add
+        ======PLAN (Nodes)=======
+['Node { object(NewYork), object(Pittsburgh), object(Toronto), object(Boston), object(Albany), connected(Pittsburgh, Boston), connected(Toronto, Albany), visited(Boston), connected(NewYork, Albany), connected(NewYork, Toronto), connected(Toronto, Pittsburgh), connected(NewYork, Boston), connected(Boston, Pittsburgh), connected(Albany, NewYork), connected(NewYork, Pittsburgh), connected(Albany, Toronto), connected(Boston, NewYork), connected(Pittsburgh, NewYork), at(Boston), connected(Toronto, NewYork), total-cost = 3 | g = 3.00 | h = inf | open = False | closed = True }', 'Node { object(NewYork), object(Pittsburgh), object(Toronto), object(Boston), object(Albany), connected(Pittsburgh, Boston), connected(Toronto, Albany), visited(Boston), connected(NewYork, Albany), connected(NewYork, Toronto), connected(Toronto, Pittsburgh), connected(NewYork, Boston), connected(Boston, Pittsburgh), connected(Albany, NewYork), connected(NewYork, Pittsburgh), connected(Albany, Toronto), connected(Boston, NewYork), connected(Pittsburgh, NewYork), connected(Toronto, NewYork), at(NewYork), visited(NewYork), total-cost = 6 | g = 6.00 | h = inf | open = False | closed = True }', 'Node { object(NewYork), object(Pittsburgh), object(Toronto), object(Boston), object(Albany), connected(Pittsburgh, Boston), connected(Toronto, Albany), visited(Boston), connected(NewYork, Albany), connected(NewYork, Toronto), connected(Toronto, Pittsburgh), at(Albany), connected(NewYork, Boston), connected(Boston, Pittsburgh), connected(Albany, NewYork), visited(Albany), connected(NewYork, Pittsburgh), connected(Albany, Toronto), connected(Boston, NewYork), connected(Pittsburgh, NewYork), connected(Toronto, NewYork), visited(NewYork), total-cost = 9 | g = 9.00 | h = inf | open = False | closed = True }', 'Node { object(NewYork), object(Pittsburgh), object(Toronto), object(Boston), object(Albany), at(Toronto), connected(Pittsburgh, Boston), connected(Toronto, Albany), visited(Boston), connected(NewYork, Albany), connected(NewYork, Toronto), connected(Toronto, Pittsburgh), connected(NewYork, Boston), connected(Boston, Pittsburgh), visited(Toronto), connected(Albany, NewYork), visited(Albany), connected(NewYork, Pittsburgh), connected(Albany, Toronto), connected(Boston, NewYork), connected(Pittsburgh, NewYork), connected(Toronto, NewYork), visited(NewYork), total-cost = 12 | g = 12.00 | h = inf | open = False | closed = True }', 'Node { object(NewYork), object(Pittsburgh), object(Toronto), object(Boston), object(Albany), connected(Pittsburgh, Boston), connected(Toronto, Albany), visited(Boston), connected(NewYork, Albany), connected(NewYork, Toronto), connected(Toronto, Pittsburgh), connected(NewYork, Boston), visited(Pittsburgh), connected(Boston, Pittsburgh), at(Pittsburgh), visited(Toronto), connected(Albany, NewYork), visited(Albany), connected(NewYork, Pittsburgh), connected(Albany, Toronto), connected(Boston, NewYork), connected(Pittsburgh, NewYork), connected(Toronto, NewYork), visited(NewYork), total-cost = 15 | g = 15.00 | h = 15.00 | open = True | closed = False }']
+        ======PLAN (Actions)=======
+('move(Pittsburgh, Boston)>', 'total-cost = 3')
+('move(Boston, NewYork)>', 'total-cost = 6')
+('move(NewYork, Albany)>', 'total-cost = 9')
+('move(Albany, Toronto)>', 'total-cost = 12')
+('move(Toronto, Pittsburgh)>', 'total-cost = 15')
+
+        ======METRICS=======
+Expanded 17 state(s).
+Opened 27 state(s).
+Reopened 0 state(s).
+Evaluated 21 state(s).
+Generated 28 state(s).
+Dead ends: 3 state(s).
+Runtime: 1.31s.
+Total heuristic runtime: 1.22s
+Computational weight of heuristic in the search: 93.45%
+

jupyddl/heuristics.py

@@ -284,7 +284,6 @@ def __init__(self, automated_planner, critical_path_level=1):
 
         if self.critical_path_level == 2:
             self.goals = [[g1, g2] for g1 in self.automated_planner.goals for g2 in self.automated_planner.goals if g1 != g2]
-            #create subgoal1, subgoal2
 
         if self.critical_path_level == 3:
             self.goals = [
@@ -297,7 +296,7 @@ def __init__(self, automated_planner, critical_path_level=1):
 
         print (self.goals)
 
-        
+
     def __h_max(self, costs):
         return max(costs)
 
@@ -370,6 +369,4 @@ def zero_heuristic():
                 else:
                     nodes[child_hash] = child
                     open_nodes_n += 1
-
-            self.automated_planner.logger.warning("!!! No path found !!!")
             return float('inf')

scripts/ipc.py

@@ -23,9 +23,11 @@
 output = sys.argv[2]
 
 apla = AutomatedPlanner(domain, problem, log_level="WARNING")
-path, metrics = apla.astar_best_first_search(heuristic_key="critical_path/2")
+path, metrics = apla.astar_best_first_search(heuristic_key="critical_path/1")
 actions = apla.get_actions_from_path(path)
 path = Path(path)
+#What happens when there is only one goal state?
+
 path2, metrics2 = apla.astar_best_first_search(heuristic_key="critical_path/1")
 actions2 = apla.get_actions_from_path(path2)
 path2 = Path(path2)