def __init__(self, cargos, planes, airports, initial: FluentState, goal: list): """ :param cargos: list of str cargos in the problem :param planes: list of str planes in the problem :param airports: list of str airports in the problem :param initial: FluentState object positive and negative literal fluents (as expr) describing initial state :param goal: list of expr literal fluents required for goal test """ self.state_map = initial.pos + initial.neg self.initial_state_TF = encode_state(initial, self.state_map) Problem.__init__(self, self.initial_state_TF, goal=goal) self.cargos = cargos self.planes = planes self.airports = airports self.actions_list = self.get_actions()
def __init__(self, cargos, planes, airports, initial: FluentState, goal: list): """ :param cargos: list of str cargos in the problem :param planes: list of str planes in the problem :param airports: list of str airports in the problem :param initial: FluentState object positive and negative literal fluents (as expr) describing initial state :param goal: list of expr literal fluents required for goal test """ self.state_map = initial.pos + initial.neg self.initial_state_TF = encode_state(initial, self.state_map) Problem.__init__(self, self.initial_state_TF, goal=goal) self.cargos = cargos self.planes = planes self.airports = airports self.actions_list = self.get_actions()
def h_levelsum(self) -> int: '''The sum of the level costs of the individual goals (admissible if goals independent) :return: int ''' level_sum = 0 # implement # for each goal in the problem, determine the level cost, then add them together for goal in self.problem.goal: graph = PlanningGraph(Problem(problem.initial_state, goal)) graph.create_graph() level_sum = level_sum + len(graph.s_levels) + len(graph.a_levels) return level_sum
def __init__(self, word_indices, probs, language_model): self.word_indices = word_indices self.language_model = language_model # weight to use for the language model # predictions self.lm_alpha = 10 # Seed minimal assumed log_s for sentences self.min_log_s = [0 for w in self.word_indices] self.min_log_s.append(0) self.keep_words = 10 # Only keep 20 most likely used words per word in the # sentence. # This should reduce search space significantly. self.word_probabilities = [ dict(sorted(p.items(), key=lambda x: x[1])[-self.keep_words:]) for p in probs ] Problem.__init__(self, (0, None, '', 0, ''))
def __init__(self, cargos, planes, airports, initial: FluentState, goal: list): """ :param cargos: list of str cargos in the problem :param planes: list of str planes in the problem :param airports: list of str airports in the problem :param initial: FluentState object positive and negative literal fluents (as expr) describing initial state :param goal: list of expr literal fluents required for goal test """ self.state_map = initial.pos + initial.neg #state_map is list of [expr(..),..] self.initial_state_TF = encode_state(initial, self.state_map) #TFTFFFTT Problem.__init__(self, self.initial_state_TF, goal=goal) #not understand self.cargos = cargos #['C1', 'C2'] self.planes = planes #['P1', 'P2'] self.airports = airports #['JFK', 'SFO'] self.actions_list = self.get_actions()
def __init__(self, cargos, planes, airports, initial: FluentState, goal: list): """ :param cargos: list of str cargos in the problem :param planes: list of str planes in the problem :param airports: list of str airports in the problem :param initial: FluentState object positive and negative literal fluents (as expr) describing initial state :param goal: list of expr literal fluents required for goal test """ self.state_map = initial.pos + initial.neg self.initial_state_TF = encode_state(initial, self.state_map) print('Now in AirCargoProblem initiation...',self.state_map, self.initial_state_TF,goal,'preparing to call problem.__init__') Problem.__init__(self, self.initial_state_TF, goal=goal) self.cargos = cargos print('Back in AirCargoProblem()........cargos,planes and airports..',cargos,planes,airports) self.planes = planes self.airports = airports self.actions_list = self.get_actions() #CALL MY ROUTINES TO BUILD LIST OF ACTIONS
def __init__(self, initial: FluentState, goal: list): self.state_map = initial.pos + initial.neg Problem.__init__(self, encode_state(initial, self.state_map), goal=goal) self.actions_list = self.get_actions()
def __init__(self, initial: FluentState, goal: list): self.state_map = initial.pos + initial.neg Problem.__init__(self, encode_state(initial, self.state_map), goal=goal) self.actions_list = self.get_actions()