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5 CONSTRAINT SATISFACTION PROBLEMS

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5CONSTRAINTSATISFACTIONPROBLEMSInwhich weseehowtreatingstatesasmore thanjustlittleblack boxesleadsto theinventionofa range ofpowerfulnew search methodsanda deeperunderstandingofproblemstructure and4 exploredtheideathatproblemscanbesolvedby searchinginaspaceofstates. Thesestatescanbeevaluatedbydomain-specif icheuristicsandtestedtoseewhetherthey ,however,eachstateis is representedbyanarbi-BLACKBOXtrarydatastr ucturethatcanbeaccessedonlybytheproblem- specificroutines thesuccessorfunction,heuristicfunction, , whosestatesandgoaltestconformtoa standard,structured,andverysimplereprese ntation( ).Searchal-REPRESENTATIONgorithmscanbede finedthattake advantageofthestructureofstatesandusegen eral-purposeratherthanproblem-specifiche uristicsto enablethesolutionoflargeproblems( ).Perhapsmostimportantly, thestandardrepresentationofthegoaltestre vealsthestruc-tureoftheproblemitself( ).Thisleadstomethodsforproblemdecomposit ionandtoanunderstandingoftheintimateconn ectionbetweenthestructureofa ,aconstraintsatisfactionproblem(orCSP)is definedbya setofvari-CONSTRAINTSATISFACTIONPROBLEMa bles,X1; X2; : : : ; Xn, anda setofconstraints,C1; C2; : : : ; Cm.

CONSTRAINT GRAPH It is helpful to visualize a CSP as a constraint graph, as shown in Figure 5.1(b). The nodes of the graph correspond to variables of the problem and the arcs correspond to constraints. Treating a problem as a CSP confers several important benefits. Because the …

  Satisfaction, Problem, Graph, Constraints, Constraint satisfaction

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