package aima.core.search.uninformed;
import java.util.Comparator;
import aima.core.search.framework.GraphSearch;
import aima.core.search.framework.Node;
import aima.core.search.framework.PrioritySearch;
import aima.core.search.framework.QueueSearch;
/**
* Artificial Intelligence A Modern Approach (3rd Edition): Figure 3.14, page
* 84.
*
*
*
* function UNIFORM-COST-SEARCH(problem) returns a solution, or failure * node <- a node with STATE = problem.INITIAL-STATE, PATH-COST = 0 * frontier <- a priority queue ordered by PATH-COST, with node as the only element * explored <- an empty set * loop do * if EMPTY?(frontier) then return failure * node <- POP(frontier) // chooses the lowest-cost node in frontier * if problem.GOAL-TEST(node.STATE) then return SOLUTION(node) * add node.STATE to explored * for each action in problem.ACTIONS(node.STATE) do * child <- CHILD-NODE(problem, node, action) * if child.STATE is not in explored or frontier then * frontier <- INSERT(child, frontier) * else if child.STATE is in frontier with higher PATH-COST then * replace that frontier node with child ** * Figure 3.14 Uniform-cost search on a graph. The algorithm is identical to the * general graph search algorithm in Figure 3.7, except for the use of a * priority queue and the addition of an extra check in case a shorter path to a * frontier state is discovered. The data structure for frontier needs to * support efficient membership testing, so it should combine the capabilities * of a priority queue and a hash table. * * @author Ciaran O'Reilly * @author Ruediger Lunde * */ public class UniformCostSearch extends PrioritySearch { public UniformCostSearch() { this(new GraphSearch()); } public UniformCostSearch(QueueSearch search) { super(search, createPathCostComparator()); } private static Comparator