def getUniformModelwithClosure(
Args,
size,
seed=None,
K_prop=[],
closure_size=1
): # generates a random uniform opponent model of given size
M = OpponentModel()
all_models = powerset(Args)
# get random seed:
if not seed:
seed = random.randint(0, sys.maxint)
M.addID(seed)
random.seed(seed)
if size > len(all_models) or size == None:
selection = all_models
else:
selection = random.sample(all_models, size)
mu = getRandomClosure(K_prop, Args, size=closure_size)
i = 0
for K in selection:
Opp = Agent(K, mu)
M.add_model(Opp, 1)
i += 1
#M.normalise()
return M
def getUniformModelwithClosure(Args,size,seed=None,K_prop=[],closure_size=1): # generates a random uniform opponent model of given size M = OpponentModel() all_models = powerset(Args) # get random seed: if not seed: seed = random.randint(0,sys.maxint) M.addID(seed) random.seed(seed) if size > len(all_models) or size==None: selection = all_models else: selection = random.sample(all_models,size) mu = getRandomClosure(K_prop,Args,size=closure_size) i = 0 for K in selection: Opp = Agent(K,mu) M.add_model(Opp,1) i += 1 #M.normalise() return M
def buildSuccess(self,semantics): X = powerset(self.Args) Y = [] for A in X: subAF = self.subframe(A) for g in self.goals: if not g in semantics(subAF).In: Y += [A] continue self.success = [set(A) for A in X if A not in Y]
def buildSuccess(self, semantics):
X = powerset(self.Args)
Y = []
for A in X:
subAF = self.subframe(A)
for g in self.goals:
if not g in semantics(subAF).In:
Y += [A]
continue
self.success = [set(A) for A in X if A not in Y]
def simulate(Opp,AF,strategy): # Simulates all possible dialogues of strategy S against Opp D_empty = Dialogue() S = strategy.copyOf() dialogue_queue = [(S,D_empty)] ineffective = None while not dialogue_queue == []: # pop first open dialogue (S,D) = dialogue_queue.pop() # do nothing if terminated if D.isTerminated(): if AF.isSuccessful(D.getArgs()): continue # dialogue is terminated and successful (do not push to queue but search continues) else: ineffective = D # D is terminated and unsuccessful (search over) break # add proponent move else: prop_move = S.play(D.getArgs()) D.add(prop_move) # do nothing if terminated if D.isTerminated(): if AF.isSuccessful(D.getArgs()): continue # dialogue is terminated and successful (do not push to queue but search continues) else: ineffective = D # D is terminated and unsuccessful (search over) break # add all possible opponent moves (no repeated arguments) else: K = Opp.getClosure().get(Opp.getKnowledge() | set(D.getArgs())) possible_moves = powerset(K-set(D.getArgs()),min_size=0) for opp_move in possible_moves: D1 = D.copyOf() D1.add(opp_move) S1 = S.copyOf() dialogue_queue += [(S1,D1)] return ineffective
def getStrategies(Prop): # generates a list of all possible sequences for simple strategies used = [] unused = [([],set([]))] while True: if unused == []: break (S, args) = unused.pop(0) used += [(S, args)] possible_moves = powerset(Prop.getKnowledge()-args, min_size=1) for move in possible_moves: new_S = S + [set(move)] new_args = args | set(move) unused += [(new_S,new_args)] return [S for (S,args) in used]
def genTestPddl(filename, AF, Prop, model, propList, cannotAssertP, dialogueP,
oPossModels, agentList, oppList, canAssertO, agentSet,
probAgent, choice): # Generates the PDDL specificatation
#I = Indexer(AF.getArgs()) # assigns a label to each subset of AF.getArgs()
with open(filename, "w") as f:
## Domain
f.write("(define (problem {0})\n".format(AF.getName()))
f.write(" (:domain StrategicArgumentation)\n")
## Objects
f.write(" (:objects {0} - arg\n".format(
str(list(Prop.getArgs())).translate(None, ",[]'")))
# Objects (proponent sets)
f.write(" ")
for S in powerset(Prop.getArgs()):
f.write("S{0} ".format(I.getIndex(S)))
f.write("- setOfArgsP\n")
# Objects (opponent sets)
f.write(" ")
for T in powerset(model.getArgs()):
f.write("T{0} ".format(I.getIndex(T)))
f.write("- setOfArgsO\n")
# Objects (Agents)
f.write(" ")
for key in oPossModels:
f.write("A{0} ".format(key))
f.write("- agent )\n\n")
## Initial Conditions
f.write(" (:init (= (stage) 0)\n\n")
## Can Assert
for a in Prop.getArgs():
f.write(" (canAssertP {0})\n".format(a))
f.write("\n")
## Opponent Model
for key in oPossModels:
if key in probAgent:
f.write(" (= (prob-belief A{0}) {1})\n".format(
key, probAgent[key]))
## Probability of Success
f.write("\n (= (prob-of-success) 0)\n\n")
## Successful Dialogues
for S in powerset(Prop.getArgs()):
for T in powerset(model.getArgs()):
if AF.isSuccessful(set(S) | set(T)):
f.write(
" (successful S{0} T{1}) ; ({2}, {3})\n".
format(I.getIndex(S), I.getIndex(T), S, T))
f.write("\n")
## Dialogue
for key in propList:
if key in dialogueP:
f.write(" (dialogueP S{0})\n".format(key))
tempKey = None
for key in oppList:
if set(oppList[key]) == choice:
tempKey = key
break
for key in oPossModels:
f.write(" (dialogueO A{0} T{1})\n".format(key, tempKey))
f.write("\n")
## Add for proponent args
for a in Prop.getArgs():
for S1 in powerset(Prop.getArgs()):
S2 = list(set(S1) | set([a]))
f.write(" (addP {0} S{1} S{2}) ; {0} + {3} = {4}\n".
format(a, I.getIndex(S1), I.getIndex(S2), S1, S2))
f.write("\n")
## Add for opponent args
for T1 in powerset(model.getArgs(), min_size=0):
for T2 in powerset(model.getArgs()):
T3 = list(set(T1) | set(T2))
f.write(
" (addO T{0} T{1} T{2}) ; {3} + {4} = {5}\n".
format(I.getIndex(T1), I.getIndex(T2), I.getIndex(T3), T1,
T2, T3))
f.write("\n")
## Closure
for key in canAssertO:
if key in agentSet:
for S1 in powerset(Prop.getArgs()):
for S2 in powerset(canAssertO[key], min_size=0,
max_size=1):
f.write(
" (canAssertO T{0} A{1} S{2}) ; A{1}: {3} -> {4}\n"
.format(I.getIndex(S2), key, I.getIndex(S1), S1,
S2))
f.write(" )\n\n")
f.write(" (:goal (> (prob-of-success) 0))\n\n")
f.write(" (:metric maximize (prob-of-success) )\n")
f.write(")")
K_prop = p.getProponentArgs()
Prop = Agent(K_prop)
K_opp = p.getOpponentArgs()
if p.getRules():
mu = ClosureFunction(p.getRules())
else:
mu = ClosureFunction([])
opponent_model = OpponentModel()
for (K, p) in p.getOpponentModel():
Ag = Agent(K, mu)
opponent_model.add_model(Ag, p)
propList = {}
for k in powerset(K_prop):
propList["{0}".format(I.getIndex(k))] = k
oppList = {}
for k in powerset(K_opp):
oppList["{0}".format(I.getIndex(k))] = k
agentList = {}
for Opp in opponent_model.getModels():
agentList["{0}".format(opponent_model.getID(Opp))] = Opp.getArgs()
probAgent = {}
for key in agentList:
