def mme_search(search, heuristic, explained_plan, m_r: Task, m_h: Task):
eps_mme = []
fringe = PrioQueue(search, heuristic, explained_plan)
c_list = []
h_list = []
fringe.push((m_r, []), 0)
m_hat = m_h
while len(fringe) != 0:
x, c = fringe.pop(m_hat)
m_hat, eps = x
m_hat_optimal = search_plan(m_hat, search, heuristic)
if m_hat_optimal is None or not is_plan_applicable(
explained_plan,
m_hat) or len(explained_plan) > len(m_hat_optimal):
h_list.append(m_hat.get_gamma() ^ m_r.get_gamma())
else:
c_list.append(m_hat.get_gamma())
for f in m_hat.get_gamma() - m_h.get_gamma():
lamb = Operator("del-{}".format(f), m_hat.get_gamma(), {}, {f})
new_gamma = lamb.apply(m_hat.get_gamma())
if new_gamma not in c_list:
sym_diff = m_hat.get_gamma() ^ m_r.get_gamma()
prop3_violated = False
for s in subsets(sym_diff):
if s in h_list:
prop3_violated = True
break
if not prop3_violated:
fringe.push((Task.from_gamma(new_gamma), eps + [lamb]),
c + 1)
if len(eps) > len(eps_mme):
eps_mme = eps
for f in m_h.get_gamma() - m_hat.get_gamma():
lamb = Operator("add-{}".format(f), m_hat.get_gamma(), {f}, {})
new_gamma = lamb.apply(m_hat.get_gamma())
if new_gamma not in c_list:
sym_diff = m_hat.get_gamma() ^ m_r.get_gamma()
prop3_violated = False
for s in subsets(sym_diff):
if s in h_list:
prop3_violated = True
break
if not prop3_violated:
fringe.push((Task.from_gamma(new_gamma), eps + [lamb]),
c + 1)
if len(eps) > len(eps_mme):
eps_mme = eps
print(m_hat.get_gamma() ^ m_r.get_gamma())
return explained_plan, eps_mme
def mce_search(search, heuristic, explained_plan, m_r: Task, m_h: Task):
c_list = [] # Closed list
p_r = explained_plan
fringe = PrioQueue(search, heuristic, explained_plan)
fringe.push((m_h, []), 0)
m_hat = m_h
i = 0
print(m_r)
while True:
print(i)
i += 1
print("Popping")
x, c = fringe.pop(m_hat)
m_hat, eps = x
print("Checking plan optimality")
p_h = search_plan(m_hat, search, heuristic)
if is_plan_optimal(p_r, m_hat, p_h):
return p_r, eps
else:
c_list.append(m_hat.get_gamma())
for f in m_hat.get_gamma() - m_r.get_gamma():
print("Fact to be removed:", f)
lamb = Operator("del-{}".format(f), m_hat.get_gamma(), {}, {f})
new_gamma = lamb.apply(m_hat.get_gamma())
if new_gamma not in c_list:
fringe.push((Task.from_gamma(new_gamma), eps + [lamb]),
c + 1)
else:
print("In c_list !")
print("Robot gamma:", m_r.get_gamma(), "\nExplored gamma: ",
m_hat.get_gamma(), "\nG(Mr) \\ G(Mh):",
m_r.get_gamma() - m_hat.get_gamma())
for f in m_r.get_gamma() - m_hat.get_gamma():
print("fact to be added: ", f)
lamb = Operator("add-{}".format(f), m_hat.get_gamma(), {f}, {})
new_gamma = lamb.apply(m_hat.get_gamma())
if new_gamma not in c_list:
fringe.push((Task.from_gamma(new_gamma), eps + [lamb]),
c + 1)
#print(Task.from_gamma(new_gamma))
else:
print("In c_list !")