LEADER 05665nam 22006974a 450 001 9910783135703321 005 20230120004133.0 010 $a1-281-04931-X 010 $a9786611049317 010 $a0-08-048932-X 035 $a(CKB)1000000000016245 035 $a(EBL)333988 035 $a(OCoLC)476138988 035 $a(SSID)ssj0000187978 035 $a(PQKBManifestationID)12011961 035 $a(PQKBTitleCode)TC0000187978 035 $a(PQKBWorkID)10143229 035 $a(PQKB)11345087 035 $a(Au-PeEL)EBL333988 035 $a(CaPaEBR)ebr10226628 035 $a(CaONFJC)MIL104931 035 $a(CaSebORM)9781558609327 035 $a(MiAaPQ)EBC333988 035 $a(EXLCZ)991000000000016245 100 $a20040311d2004 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aKnowledge representation and reasoning$b[electronic resource] /$fRonald J. Brachman, Hector J. Levesque 205 $a1st edition 210 $aAmsterdam ;$aBoston $cMorgan Kaufmann$dc2004 215 $a1 online resource (413 p.) 225 1 $aThe Morgan Kaufmann Series in Artificial Intelligence 300 $aDescription based upon print version of record. 311 $a1-4933-0379-1 311 $a1-55860-932-6 320 $aIncludes bibliographical references (p. 349-375) and index. 327 $aFront Cover; Knowledge Representation and Reasoning; Copyright Page; Contents; Preface; Acknowledgments; Chapter 1. Introduction; 1.1 The Key Concepts: Knowledge, Representation, and Reasoning; 1.2 Why Knowledge Representation and Reasoning?; 1.3 The Role of Logic; 1.4 Bibliographic Notes; 1.5 Exercises; Chapter 2. The Language of First-Order Logic; 2.1 Introduction; 2.2 The Syntax; 2.3 The Semantics; 2.4 The Pragmatics; 2.5 Explicit and Implicit Belief; 2.6 Bibliographic Notes; 2.7 Exercises; Chapter 3. Expressing Knowledge; 3.1 Knowledge Engineering; 3.2 Vocabulary; 3.3 Basic Facts 327 $a3.4 Complex Facts3.5 Terminological Facts; 3.6 Entailments; 3.7 Abstract Individuals; 3.8 Other Sorts of Facts; 3.9 Bibliographic Notes; 3.10 Exercises; Chapter 4. Resolution; 4.1 The Propositional Case; 4.2 Handling Variables and Quantifiers; 4.3 Dealing with Computational Intractability; 4.4 Bibliographic Notes; 4.5 Exercises; Chapter 5. Reasoning with Horn Clauses; 5.1 Horn Clauses; 5.2 SLD Resolution; 5.3 Computing SLD Derivations; 5.4 Bibliographic Notes; 5.5 Exercises; Chapter 6. Procedural Control of Reasoning; 6.1 Facts and Rules; 6.2 Rule Formation and Search Strategy 327 $a6.3 Algorithm Design6.4 Specifying Goal Order; 6.5 Committing to Proof Methods; 6.6 Controlling Backtracking; 6.7 Negation as Failure; 6.8 Dynamic Databases; 6.9 Bibliographic Notes; 6.10 Exercises; Chapter 7. Rules in Production Systems; 7.1 Production Systems: Basic Operation; 7.2 Working Memory; 7.3 Production Rules; 7.4 A First Example; 7.5 A Second Example; 7.6 Conflict Resolution; 7.7 Making Production Systems More Efficient; 7.8 Applications and Advantages; 7.9 Some Significant Production Rule Systems; 7.10 Bibliographic Notes; 7.11 Exercises; Chapter 8. Object-Oriented Representation 327 $a8.1 Objects and Frames8.2 A Basic Frame Formalism; 8.3 An Example: Using Frames to Plan a Trip; 8.4 Beyond the Basics; 8.5 Bibliographic Notes; 8.6 Exercises; Chapter 9. Structured Descriptions; 9.1 Descriptions; 9.2 A Description Language; 9.3 Meaning and Entailment; 9.4 Computing Entailments; 9.5 Taxonomies and Classification; 9.6 Beyond the Basics; 9.7 Bibliographic Notes; 9.8 Exercises; Chapter 10. Inheritance; 10.1 Inheritance Networks; 10.2 Strategies for Defeasible Inheritance; 10.3 A Formal Account of Inheritance Networks; 10.4 Bibliographic Notes; 10.5 Exercises; Chapter 11. Defaults 327 $a11.1 Introduction11.2 Closed-World Reasoning; 11.3 Circumscription; 11.4 Default Logic; 11.5 Autoepistemic Logic; 11.6 Conclusion; 11.7 Bibliographic Notes; 11.8 Exercises; Chapter 12. Vagueness, Uncertainty, and Degrees of Belief; 12.1 Noncategorical Reasoning; 12.2 Objective Probability; 12.3 Subjective Probability; 12.4 Vagueness; 12.5 Bibliographic Notes; 12.6 Exercises; Chapter 13. Explanation and Diagnosis; 13.1 Diagnosis; 13.2 Explanation; 13.3 A Circuit Example; 13.4 Beyond the Basics; 13.5 Bibliographic Notes; 13.6 Exercises; Chapter 14. Actions; 14.1 The Situation Calculus 327 $a14.2 A Simple Solution to the Frame Problem 330 $aKnowledge representation is at the very core of a radical idea for understanding intelligence. Instead of trying to understand or build brains from the bottom up, its goal is to understand and build intelligent behavior from the top down, putting the focus on what an agent needs to know in order to behave intelligently, how this knowledge can be represented symbolically, and how automated reasoning procedures can make this knowledge available as needed. This landmark text takes the central concepts of knowledge representation developed over the last 50 years and illustrates them in a l 410 0$aMorgan Kaufmann Series in Artificial Intelligence 606 $aKnowledge representation (Information theory) 606 $aReasoning 615 0$aKnowledge representation (Information theory) 615 0$aReasoning. 676 $a006.3/32 700 $aBrachman$b Ronald J.$f1949-$051202 701 $aLevesque$b Hector J.$f1951-$051203 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910783135703321 996 $aKnowledge representation and reasoning$93794979 997 $aUNINA