Update: print certainty values properly

Author: patham9

main.py

@@ -37,7 +37,7 @@
 if __name__ == "__main__":
     for Time in range(300):
         start_time = time.time()
-        usedRules, FocusSet, RuleEvidence, loc, observed_world, rules, negrules, world, debuginput = NACE_Cycle(Time, FocusSet, RuleEvidence, loc, observed_world, rules, negrules, deepcopy(world))
+        usedRules, FocusSet, RuleEvidence, loc, observed_world, rules, negrules, world, debuginput, values = NACE_Cycle(Time, FocusSet, RuleEvidence, loc, observed_world, rules, negrules, deepcopy(world))
         end_time = time.time()
         print("VALUES", world[VALUES], "FOCUS SET", FocusSet)
         elapsed_time = end_time - start_time
@@ -46,13 +46,13 @@
         if "debug" in sys.argv and debuginput != "" and debuginput not in ["w", "a", "s", "d", "l", "p"]:
             predworld = deepcopy(observed_world)
             score = 0.0
-            values = []
             while True:
                 print("\033[1;1H\033[2J")
                 print("\033[0mImagined map:\033[97;43m")
                 World_Print(predworld)
                 print("\033[0m")
-                print("score:", -score, "values", values)
+                NACE_PrintScore(score)
+                print("values:", values)
                 d = input()
                 score = 0.0
                 if d == 'q':

nace.py

@@ -60,10 +60,10 @@ def NACE_Cycle(Time, FocusSet, RuleEvidence, loc, observed_world, rulesin, negru
                 action = favoured_actions_for_revisit[0]
                 plan = favoured_actions_for_revisit
             else:
-                print("BABBLE", "score:", airis_score)
+                print("BABBLE")
                 action = random.choice(actions) #motorbabbling
         else:
-            print("ACHIEVE" if airis_score == float("-inf") else "CURIOUS", Prettyprint_Plan(favoured_actions), "score:", -airis_score)#, rules)
+            print("ACHIEVE" if airis_score == float("-inf") else "CURIOUS", Prettyprint_Plan(favoured_actions), end=" "); NACE_PrintScore(airis_score)
             action = favoured_actions[0]
             plan = favoured_actions
     else:
@@ -84,7 +84,7 @@ def NACE_Cycle(Time, FocusSet, RuleEvidence, loc, observed_world, rulesin, negru
     loc, newworld = World_Move(loc, deepcopy(oldworld), action)
     observed_world_old = deepcopy(observed_world)
     observed_world = World_FieldOfView(Time, loc, observed_world, newworld)
-    predicted_world, _, __, ___ = NACE_Predict(Time, FocusSet, deepcopy(observed_world_old), action, rules)
+    predicted_world, _, __, values = NACE_Predict(Time, FocusSet, deepcopy(observed_world_old), action, rules)
     if "manual" not in sys.argv:
         print(f"\033[0mWorld t={Time} beliefs={len(rules)}:\033[97;40m")
         World_Print(newworld)
@@ -109,7 +109,7 @@ def NACE_Cycle(Time, FocusSet, RuleEvidence, loc, observed_world, rulesin, negru
             newrules.add(rule)
     else:
         usedRules = newrules = newnegrules = rules
-    return usedRules, FocusSet, RuleEvidence, loc, observed_world, newrules, newnegrules, newworld, debuginput
+    return usedRules, FocusSet, RuleEvidence, loc, observed_world, newrules, newnegrules, newworld, debuginput, values
 
 # Apply move to the predicted world model whereby we use the learned tules to decide how grid elements might change most likely
 def NACE_Predict(Time, FocusSet, oldworld, action, rules, customGoal = None):
@@ -370,3 +370,10 @@ def _RuleApplicable(scores, highscore, highesthighscore, rule):
     if highscore > 0.0 and scores.get(rule, 0.0) == highesthighscore:
         return True
     return False
+
+#Print score value taking its semantics regarding its value range semantics for planning into account
+def NACE_PrintScore(score):
+    if score >= 0.0 and score <= 1.0:
+        print("certainty:", score)
+    else:
+        print("desired: True")

prettyprint.py

@@ -31,15 +31,15 @@ def Prettyprint_rule(RuleEvidence, Hypothesis_TruthValue, rule):
     precons = actions_values_preconditions[2:]
     print("<(", end="")
     for i, x in enumerate(actions_values_preconditions[1]):
-        name = "keys"
+        name = "var" + str(i)
         print(f"{_prettyVarValue(name, x)}", end="")
         print(f" &| ", end="")
     for i, x in enumerate(precons):
         print(f"{_prettyTriplet(x)}", end="")
         if i != len(precons)-1:
             print(f" &| ", end="")
     scoreInc = f"<s_{rule[1][3][0]} --> scorePlus>"
-    keys = f"<k_{rule[1][3][1]} --> keys>"
+    keys = f"<k_{rule[1][3][1]} --> var0>"
     print(") &/", action, "=/> (" + _prettyTriplet(rule[1]) + " &| " + scoreInc + " &| " + keys + ")>.", Hypothesis_TruthValue(RuleEvidence[rule]))
 
 def _prettyVarValue(name, value):