reasoning with constraints given cspthe stochastic local searcher (slsearchercreates the data structuresvariables to select is the set of all of the variables with domain-size greater than one for variable not in this setwe cannot pick another value from that variable var to constraints maps from variable into the set of constraints it is involved in note that the inverse mapping from constraints into variables is part of the definition of constraint cspsls py -stochastic local search for solving csps from cspproblem import cspconstraint from searchproblem import arcsearch_problem from display import displayable import random import heapq class slsearcher(displayable)""" search problem directly from the csp node is variable:value dictionary""def __init__(selfcsp)self csp csp self variables_to_select {var for var in self csp variables if len(var domain create assignment and conflicts set self current_assignment none this will trigger random restart self number_of_steps #number of steps after the initialization restart creates new total assignmentand constructs the set of conflicts (the constraints that are false in this assignmentcspsls py -(continued def restart(self)"""creates new total assignment and the conflict set ""self current_assignment {var:random_choice(var domainfor var in self csp variablesself display( ,"initial assignment",self current_assignmentself conflicts set(for con in self csp constraintsif not con holds(self current_assignment)self conflicts add(conself display( ,"number of conflicts",len(self conflicts)self variable_pq none the search method is the top-level searching algorithm it can either be used to start the search or to continue searching if there is no current assignmentversion june