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()
