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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.

In some cases, we can reduce CONSTRAINTS integer constraint problems to finite-domain problems simply by bounding the values of all the variables. For example, in a scheduling problem, we can set an upper bound equal to the total length of all the jobs to be scheduled.

  Satisfaction, Problem, Some, Constraints, Constraint satisfaction problems

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