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5 CONSTRAINT SATISFACTION PROBLEMS

5 CONSTRAINTSATISFACTIONPROBLEMSI nwhich weseehowtreatingstatesasmore thanjustlittleblack boxesleadsto theinventionofa range ofpowerfulnew search methodsanda deeperunderstandingofproblemstructure and4 exploredtheideathatproblemscanbesolvedby searchinginaspaceofstates. Thesestatescanbeevaluatedbydomain-specif icheuristicsandtestedtoseewhetherthey ,however,eachstateis is representedbyanarbi-BLACKBOX trarydatastructurethatcanbeaccessedonlyb ytheproblem-specificroutines thesuccessorfunction,heuristicfunction, , whosestatesandgoaltestconformtoa standard,structured,andverysimplereprese ntation( ).Searchal-REPRESENTATION gorithmscanbedefinedthattake advantageofthestructureofstatesandusegen eral-purposeratherthanproblem-specifiche uristicsto enablethesolutionoflargeproblems( ).Perhapsmostimportantly, thestandardrepresentationofthegoaltestre vealsthestruc-tureoftheproblemitself( ).Thisleadstomethodsforproblemdecomposit ionandtoanunderstandingoftheintimateconn ectionbetweenthestructureofa ,aconstraintsatisfactionproblem(orCSP)is definedbya setofvari-CONSTRAINTSATISFACTIONPROBLEM ables,X1; X2; : : : ; Xn, anda setofconstraints,C1; C2; : : : ; Cm.

A complete assignment is one in which every variable is mentioned, and a so- ... must obey a variety of astronomical, precedence, and power constraints. The best-known ... order conic programming, and so on. In addition to examining the types of …

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INTRODUCTION

aima.cs.berkeley.edu

CHAPTER 3 SOLVING PROBLEMS BY SEARCHING function BEST-FIRST-SEARCH(problem,f) returns a solution node or failure node ←NODE(STATE=problem.INITIAL) frontier ←a priority queue orderedby f, with node as an element reached ←a lookup table, with one entry with key problem.INITIAL and value node while not IS-EMPTY(frontier) do node ←POP(frontier) if …

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20 STATISTICAL LEARNING METHODS - Artificial intelligence

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aima.cs.berkeley.edu

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