def setUp(self):
self.p1 = air_cargo_p1()
self.act1 = Action(
expr('Load(C1, P1, SFO)'),
[[expr('At(C1, SFO)'), expr('At(P1, SFO)')], []],
[[expr('In(C1, P1)')], [expr('At(C1, SFO)')]]
)
def setUp(self):
self.p1 = air_cargo_p1()
self.act1 = Action(
expr('Load(C1, P1, SFO)'),
[[expr('At(C1, SFO)'), expr('At(P1, SFO)')], []],
[[expr('In(C1, P1)')], [expr('At(C1, SFO)')]]
)
def setUp(self):
self.p1 = air_cargo_p1(
) #Execute air caogo 1 initiation including building list of actions
self.act1 = Action(
expr('Load(C1, P1, SFO)'),
[[expr('At(C1, SFO)'), expr('At(P1, SFO)')], []],
[[expr('In(C1, P1)')], [expr('At(C1, SFO)')]])
def compare():
print("Comparative")
#print(PROBLEMS)
#print(SEARCHES)
#main(['1', '2', '3'], ['1', '2', '3', '4', '5'])
compare_searchers(
[air_cargo_p1(), air_cargo_p2(),
air_cargo_p3()], ["ACP1", "ACP2", "ACP3"], [
breadth_first_search,
depth_first_graph_search,
])
def setUp(self):
self.p1 = air_cargo_p1()
from my_air_cargo_problems import (
air_cargo_p1,
air_cargo_p2,
air_cargo_p3,
)
from aimacode.search import (breadth_first_search, astar_search,
breadth_first_tree_search,
depth_first_graph_search, uniform_cost_search,
greedy_best_first_graph_search,
depth_limited_search, recursive_best_first_search)
from run_search import run_search
if __name__ == '__main__':
p1 = air_cargo_p1()
print("Initial state for this problem is {}".format(p1.initial))
#print("Actions for this domain are:")
#for a in p1.actions_list:
# print(' {}{}'.format(a.name, a.args))
#print("Fluents in this problem are:")
#for f in p1.state_map:
# print(' {}'.format(f))
print("Goal requirement for this problem are:")
for g in p1.goal:
print(' {}'.format(g))
print()
print("*** A-star ignore preconditions heuristic")
run_search(p1, astar_search, p1.h_ignore_preconditions)
print("A-star levelsum heuristic")
run_search(p1, astar_search, p1.h_pg_levelsum)
import argparse
from timeit import default_timer as timer
from aimacode.search import InstrumentedProblem
from aimacode.search import (breadth_first_search, astar_search,
breadth_first_tree_search, depth_first_graph_search, uniform_cost_search,
greedy_best_first_graph_search, depth_limited_search,
recursive_best_first_search,Node)
from my_air_cargo_problems import air_cargo_p1, air_cargo_p2, air_cargo_p3
from my_planning_graph import PlanningGraph
from run_search import run_search
print(air_cargo_p1)
#node = Node(air_cargo_p1.initial)
#print(node)
p = air_cargo_p1()
print("**** Have Cake example problem setup ****")
print("Initial state for this problem is {}".format(p.initial))
print("Actions for this domain are:")
for a in p.actions_list:
print(' {}{}'.format(a.name, a.args))
print("Fluents in this problem are:")
for f in p.state_map:
print(' {}'.format(f))
print("Goal requirement for this problem are:")
for g in p.goal:
print(' {}'.format(g))
print()
print("*** Breadth First Search")
run_search(p, breadth_first_search)
print("*** Depth First Search")
def setUp(self):
self.p1 = air_cargo_p1()
def setUp(self):
self.p = have_cake()
self.pg = PlanningGraph(self.p, self.p.initial)
def test_add_action_level(self):
for level, nodeset in enumerate(self.pg.a_levels):
for node in nodeset:
print("Level {}: {}{})".format(level, node.action.name,
node.action.args))
#self.assertEqual(len(self.pg.a_levels[0]), 3, len(self.pg.a_levels[0]))
#self.assertEqual(len(self.pg.a_levels[1]), 6, len(self.pg.a_levels[1]))
if __name__ == '__main__':
p = air_cargo_p1()
print("Initial state for this problem is {}".format(p.initial))
print("Actions for this domain are:")
print(len(p.initial))
print(len(p.state_map))
for a in p.actions_list:
print(' {}{}'.format(a.name, a.args))
print("Fluents in this problem are:")
for f in p.state_map:
print(' {}'.format(f))
print("Goal requirement for this problem are:")
for g in p.goal:
print(' {}'.format(g))
#print(decode_state(p.initial, p.state_map))
p2 = air_cargo_p1()
