reasoning with constraints cspsearch py -representations of search problem from csp from cspproblem import cspconstraint from searchproblem import arcsearch_problem from utilities import dict_union class search_from_csp(search_problem)""" search problem directly from the csp node is variable:value dictionary""def __init__(selfcspvariable_order=none)self csp=csp if variable_orderassert set(variable_order=set(csp variablesassert len(variable_order=len(csp variablesself variables variable_order elseself variables list(csp variables def is_goal(selfnode)"""returns whether the current node is goal for the search ""return len(node)==len(self csp variables def start_node(self)"""returns the start node for the search ""return {the neighbors(nodemethod uses the fact that the length of the nodewhich is the number of variables already assignedis the index of the next variable to split on note that we do no need to check whether there are no more variables to split onas the nodes are all consistentby constructionand so when there are no more variables we have solutionand so don' need the neighbours cspsearch py -(continued def neighbors(selfnode)"""returns list of the neighboring nodes of node ""var self variables[len(node)the next variable res [for val in var domainnew_env dict_union(node,{var:val}#dictionary union if self csp consistent(new_env)res append(arc(node,new_env)return res the unit tests relies on solver the following procedure creates solver using search that can be tested cspsearch py -(continued from cspexamples import csp ,csp ,test_cspcrossword crossword version june