def goal_test(self, state: str) -> bool:
kb = PropKB()
kb.tell(decode_state(state, self.state_map).pos_sentence())
for clause in self.goal:
if clause not in kb.clauses:
return False
return True
def actions(self, state: str) -> list: # of Action
possible_actions = []
kb = PropKB()
kb.tell(decode_state(state, self.state_map).pos_sentence())
for action in self.actions_list:
is_possible = True
for clause in action.precond_pos:
if clause not in kb.clauses:
is_possible = False
for clause in action.precond_neg:
if clause in kb.clauses:
is_possible = False
if is_possible:
possible_actions.append(action)
return possible_actions
def actions(self, state: str) -> list:
# TODO Part 2 implement (see PlanningProblem in helpers.planning_problem)
possible_actions = []
kb = PropKB()
kb.tell(decode_state(state, self.state_map).pos_sentence())
for action in self.actions_list:
is_possible = True
for clause in action.precond_pos:
if clause not in kb.clauses:
is_possible = False
for clause in action.precond_neg:
if clause in kb.clauses:
is_possible = False
if is_possible:
possible_actions.append(action)
return possible_actions
def result(self, state: str, action: Action):
new_state = FluentState([], [])
old_state = decode_state(state, self.state_map)
for fluent in old_state.pos:
if fluent not in action.effect_rem:
new_state.pos.append(fluent)
for fluent in action.effect_add:
if fluent not in new_state.pos:
new_state.pos.append(fluent)
for fluent in old_state.neg:
if fluent not in action.effect_add:
new_state.neg.append(fluent)
for fluent in action.effect_rem:
if fluent not in new_state.neg:
new_state.neg.append(fluent)
return encode_state(new_state, self.state_map)
def result(self, state: str, action: Action):
# TODO implement (see PlanningProblem in helpers.planning_problem)
new_state = FluentState([], [])
old_state = decode_state(state, self.state_map)
for fluent in old_state.pos:
if fluent not in action.effect_rem:
new_state.pos.append(fluent)
for fluent in action.effect_add:
if fluent not in new_state.pos:
new_state.pos.append(fluent)
for fluent in old_state.neg:
if fluent not in action.effect_add:
new_state.neg.append(fluent)
for fluent in action.effect_rem:
if fluent not in new_state.neg:
new_state.neg.append(fluent)
return encode_state(new_state, self.state_map)
def __init__(self,
problem: PlanningProblem,
state: str,
serial_planning=True):
'''
:param problem: PlanningProblem (or subclass such as AirCargoProblem or HaveCakeProblem)
:param state: str (will be in form TFTTFF... representing fluent states)
:param serial_planning: bool (whether or not to assume that only one action can occur at a time)
Instance variable calculated:
fs: FluentState
the state represented as positive and negative fluent literal lists
all_actions: list of the PlanningProblem valid ground actions combined with calculated no-op actions
s_levels: list of sets of PgNode_s, where each set in the list represents an S-level in the planning graph
a_levels: list of sets of PgNode_a, where each set in the list represents an A-level in the planning graph
'''
self.problem = problem
self.fs = decode_state(state, problem.state_map)
self.serial = serial_planning
self.all_actions = self.problem.actions_list + self.noop_actions(
self.problem.state_map)
self.s_levels = []
self.a_levels = []
self.create_graph()
def test_AC_result(self):
fs = decode_state(self.p1.result(self.p1.initial, self.act1),
self.p1.state_map)
self.assertTrue(expr('In(C1, P1)') in fs.pos)
self.assertTrue(expr('At(C1, SFO)') in fs.neg)
