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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( ).

SATISFACTION PROBLEMS In which we see how treating states as more than just little black boxes leads to the invention of a range of powerful new search methods and a deeper understanding of problem structure and complexity. Chapters 3 and 4 explored the idea that problems can be solved by searching in a space of states.

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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

aima.cs.berkeley.edu

20 STATISTICAL LEARNING METHODS In which we view learning as a form of uncertain reasoning from observations. Part V pointed out the prevalence of uncertainty in real environments.

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Artificial Intelligence

aima.cs.berkeley.edu

PEARSONSERIES IN ARTIFICIAL INTELLIGENCE Stuart Russell and Peter Norvig, Editors FORSYTH & PONCE Computer Vision: A Modern Approach, 2nd ed. GRAHAM ANSI Common Lisp JURAFSKY & MARTIN Speech and Language Processing, 2nd ed. NEAPOLITAN Learning Bayesian Networks RUSSELL & NORVIG Artificial Intelligence: A Modern Approach, 4th ed.

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7 LOGICAL AGENTS

aima.cs.berkeley.edu

all the basic concepts of logic. There is also a well-developed technology for reasoning in propositional logic, which we describe in sections 7.5 and 7.6. Finally, Section 7.7 combines the concept of logical agents with the technology of propositional logic to build some simple agents for the wumpus world.

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