from stripsproblem import delivery_domain from cspconsistency import search_with_ac_from_cspcon_solver from stripsproblem import planning_problemproblem problem problem blocks blocks blocks problem con_plan(problem , should it succeedcon_plan(problem , should it succeedcon_plan(problem , should it succeedto use search to enumerate solutions #searcher searcher(search_with_ac_from_csp(csp_from_strips(problem ))#print(searcher search()returns path to solution #problem con_plan(problem , should it succeedcon_plan(problem , should it succeed#to use search to enumerate solutions#searcher searcher(search_with_ac_from_csp(csp_from_strips(problem ))#print(searcher search()returns path to solution #problem #con_plan(problem should fail?#con_plan(problem should succeed?? #example problem planning_problem(delivery_domain{'swc':true'rhc':false}{'swc':false}#con_plan(problem , horizon of #con_plan(problem , horizon of problem planning_problem(delivery_domain,{'swc':true}{'swc':false'mw':false'rhm':false} for the stochastic local search#from cspsls import slsearcherruntime_distribution cspplanning csp_from_strips(problem should succeed #se slsearcher(cspplanning )print(se search( , )# runtime_distribution(cspplanning # plot_runs( , , warning will take few minutes partial-order planning to run the demoin folder "aipython"load "stripspop py"and copy and paste the commented-out example queries at the bottom of that file version june