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my_air_cargo_problems.py
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/
my_air_cargo_problems.py
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from aimacode.logic import PropKB
from aimacode.planning import Action
from aimacode.search import (
Node, Problem,
)
from aimacode.utils import expr
from lp_utils import (
FluentState, encode_state, decode_state,
)
from my_planning_graph import PlanningGraph
from functools import lru_cache
class AirCargoProblem(Problem):
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 get_actions(self):
"""
This method creates concrete actions (no variables) for all actions in the problem
domain action schema and turns them into complete Action objects as defined in the
aimacode.planning module. It is computationally expensive to call this method directly;
however, it is called in the constructor and the results cached in the `actions_list` property.
Returns:
----------
list<Action>
list of Action objects
"""
# TODO create concrete Action objects based on the domain action schema for: Load, Unload, and Fly
# concrete actions definition: specific literal action that does not include variables as with the schema
# for example, the action schema 'Load(c, p, a)' can represent the concrete actions 'Load(C1, P1, SFO)'
# or 'Load(C2, P2, JFK)'. The actions for the planning problem must be concrete because the problems in
# forward search and Planning Graphs must use Propositional Logic
def load_actions():
"""Create all concrete Load actions and return a list
:return: list of Action objects
"""
loads = []
# TODO create all load ground actions from the domain Load action
return loads
def unload_actions():
"""Create all concrete Unload actions and return a list
:return: list of Action objects
"""
unloads = []
# TODO create all Unload ground actions from the domain Unload action
return unloads
def fly_actions():
"""Create all concrete Fly actions and return a list
:return: list of Action objects
"""
flys = []
for fr in self.airports:
for to in self.airports:
if fr != to:
for p in self.planes:
precond_pos = [expr("At({}, {})".format(p, fr)),
]
precond_neg = []
effect_add = [expr("At({}, {})".format(p, to))]
effect_rem = [expr("At({}, {})".format(p, fr))]
fly = Action(expr("Fly({}, {}, {})".format(p, fr, to)),
[precond_pos, precond_neg],
[effect_add, effect_rem])
flys.append(fly)
return flys
return load_actions() + unload_actions() + fly_actions()
def actions(self, state: str) -> list:
""" Return the actions that can be executed in the given state.
:param state: str
state represented as T/F string of mapped fluents (state variables)
e.g. 'FTTTFF'
:return: list of Action objects
"""
# TODO implement
possible_actions = []
return possible_actions
def result(self, state: str, action: Action):
""" Return the state that results from executing the given
action in the given state. The action must be one of
self.actions(state).
:param state: state entering node
:param action: Action applied
:return: resulting state after action
"""
# TODO implement
new_state = FluentState([], [])
return encode_state(new_state, self.state_map)
def goal_test(self, state: str) -> bool:
""" Test the state to see if goal is reached
:param state: str representing state
:return: 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 h_1(self, node: Node):
# note that this is not a true heuristic
h_const = 1
return h_const
@lru_cache(maxsize=8192)
def h_pg_levelsum(self, node: Node):
"""This heuristic uses a planning graph representation of the problem
state space to estimate the sum of all actions that must be carried
out from the current state in order to satisfy each individual goal
condition.
"""
# requires implemented PlanningGraph class
pg = PlanningGraph(self, node.state)
pg_levelsum = pg.h_levelsum()
return pg_levelsum
@lru_cache(maxsize=8192)
def h_ignore_preconditions(self, node: Node):
"""This heuristic estimates the minimum number of actions that must be
carried out from the current state in order to satisfy all of the goal
conditions by ignoring the preconditions required for an action to be
executed.
"""
# TODO implement (see Russell-Norvig Ed-3 10.2.3 or Russell-Norvig Ed-2 11.2)
count = 0
return count
def air_cargo_p1() -> AirCargoProblem:
cargos = ['C1', 'C2']
planes = ['P1', 'P2']
airports = ['JFK', 'SFO']
pos = [expr('At(C1, SFO)'),
expr('At(C2, JFK)'),
expr('At(P1, SFO)'),
expr('At(P2, JFK)'),
]
neg = [expr('At(C2, SFO)'),
expr('In(C2, P1)'),
expr('In(C2, P2)'),
expr('At(C1, JFK)'),
expr('In(C1, P1)'),
expr('In(C1, P2)'),
expr('At(P1, JFK)'),
expr('At(P2, SFO)'),
]
init = FluentState(pos, neg)
goal = [expr('At(C1, JFK)'),
expr('At(C2, SFO)'),
]
return AirCargoProblem(cargos, planes, airports, init, goal)
def air_cargo_p2() -> AirCargoProblem:
# TODO implement Problem 2 definition
pass
def air_cargo_p3() -> AirCargoProblem:
# TODO implement Problem 3 definition
pass