def run(problem,name):
print("\n\n*******",name)
print("\nA*:")
asearcher = AStarSearcher(problem)
print("Path found:",asearcher.search()," cost=",asearcher.solution.cost)
print("there are",asearcher.frontier.count(asearcher.solution.cost),
"elements remaining on the queue with f-value=",asearcher.solution.cost)
print("\nA* with MPP:"),
msearcher = SearcherMPP(problem)
print("Path found:",msearcher.search()," cost=",msearcher.solution.cost)
print("there are",msearcher.frontier.count(msearcher.solution.cost),
"elements remaining on the queue with f-value=",msearcher.solution.cost)
bound = asearcher.solution.cost+0.01
print("\nBranch and bound (with too-good initial bound of", bound,")")
tbb = DF_branch_and_bound(problem,bound) # cheating!!!!
print("Path found:",tbb.search()," cost=",tbb.solution.cost)
print("Rerunning B&B")
print("Path found:",tbb.search())
bbound = asearcher.solution.cost*2+10
print("\nBranch and bound (with not-very-good initial bound of", bbound, ")")
tbb2 = DF_branch_and_bound(problem,bbound) # cheating!!!!
print("Path found:",tbb2.search()," cost=",tbb2.solution.cost)
print("Rerunning B&B")
print("Path found:",tbb2.search())
print("\nDepth-first search: (Use ^C if it goes on forever)")
tsearcher = Searcher(problem)
print("Path found:",tsearcher.search()," cost=",tsearcher.solution.cost)
def test_forward_heuristic(thisproblem=problem1):
print("\n***** FORWARD NO HEURISTIC")
print(SearcherMPP(Forward_STRIPS(thisproblem)).search())
print("\n***** FORWARD WITH HEURISTIC h1")
print(SearcherMPP(Forward_STRIPS(thisproblem, h1)).search())
print("\n***** FORWARD WITH HEURISTICs h1 and h2")
print(SearcherMPP(Forward_STRIPS(thisproblem, maxh(h1, h2))).search())
def run(problem, name):
print("\n\n*******", name)
print("\nA*:")
tsearcher = Searcher(problem)
print("Path found:", tsearcher.search(), " cost=",
tsearcher.solution.cost)
print("there are", tsearcher.frontier.count(tsearcher.solution.cost),
"elements remaining on the queue with f-value=",
tsearcher.solution.cost)
print("\nA* with MPP:"),
msearcher = SearcherMPP(problem)
print("Path found:", msearcher.search(), " cost=",
msearcher.solution.cost)
print("there are", msearcher.frontier.count(msearcher.solution.cost),
"elements remaining on the queue with f-value=",
msearcher.solution.cost)
print("\nBranch and bound (with too-good initial bound):")
tbb = DF_branch_and_bound(problem,
tsearcher.solution.cost + 0.1) # cheating!!!!
print("Path found:", tbb.search(), " cost=", tbb.solution.cost)
def test_regression_heuristic(thisproblem=problem1):
print("\n***** REGRESSION NO HEURISTIC")
print(SearcherMPP(Regression_STRIPS(thisproblem)).search())
print("\n***** REGRESSION WITH HEURISTICs h1 and h2")
print(SearcherMPP(Regression_STRIPS(thisproblem, maxh(h1, h2))).search())
return newconst
def possible(self, pair, constraint):
(x, y) = pair
return (y, x) not in constraint
from searchBranchAndBound import DF_branch_and_bound
from searchMPP import SearcherMPP
from stripsProblem import problem0, problem1, problem2, blocks1, blocks2, blocks3
rplanning0 = POP_search_from_STRIPS(problem0)
rplanning1 = POP_search_from_STRIPS(problem1)
rplanning2 = POP_search_from_STRIPS(problem2)
searcher0 = DF_branch_and_bound(rplanning0, 5)
searcher0a = SearcherMPP(rplanning0)
searcher1 = DF_branch_and_bound(rplanning1, 10)
searcher1a = SearcherMPP(rplanning1)
searcher2 = DF_branch_and_bound(rplanning2, 10)
searcher2a = SearcherMPP(rplanning2)
# Try one of the following searchers
# a = searcher0.search()
# a = searcher0a.search()
# a.end().extract_plan() # print a plan found
# a.end().constraints # print the constraints
# SearcherMPP.max_display_level = 0 # less detailed display
# DF_branch_and_bound.max_display_level = 0 # less detailed display
# a = searcher1.search()
# a = searcher1a.search()
# a = searcher2.search()
# a = searcher2a.search()
'cash_pop_out_from_T3':
Strips({'REQUEST_SENT_FROM_T3': True}, {'RESPONSE_SENT_TO_T3': True}),
'close_T3_connection':
Strips({'RESPONSE_SENT_TO_T3': True}, {
'T3_CONNECTTED': False,
'SERVER_LISTENING': False,
'FREE_SOCKET_T3': False
}),
})
case1 = Planning_problem(domain, {'T_IDLE': 'T1'}, {
'RESPONSE_SENT_TO_T1': True,
'T1_CONNECTTED': False
})
case1_solution1 = SearcherMPP(Forward_STRIPS(case1)).search()
case1_solution2 = SearcherMPP(Regression_STRIPS(case1)).search()
print('---------')
print('case1 forward planner solution:')
print(case1_solution1)
print('---------')
print('case1 regression planner solution:')
print(case1_solution2)
print('---------')
case2 = Planning_problem(domain, {'T_IDLE': 'T2_T3'}, {
'RESPONSE_SENT_TO_T3': True,
'RESPONSE_SENT_TO_T2': True,
'T3_CONNECTTED': False,
'T2_CONNECTTED': False