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Logical foundations of artificial intelligence /
Intended both as a text for advanced undergraduates and graduate students, and as a key reference work for AI researchers and developers, Logical Foundations of Artificial Intelligence is a lucid, rigorous, and comprehensive account of the fundamentals of artificial intelligence from the standpoint...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Los Altos, Calif. :
Morgan Kaufmann,
[1987]
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Front Cover; Logical Foundations of Artificial Intelligence; Copyright Page; Table of Contents; Typographical Conventions; Chapter 1. Introduction; 1.1 Bibliographical and Historical Remarks; Exercises; Chapter 2. Declarative Knowledge; 2.1 Conceptualization; 2.2 Predicate Calculus; 2.3 Semantics; 2.4 Blocks World Example; 2.5 Circuits Example; 2.6 Algebraic Examples; 2.7 List Examples; 2.8 Natural-Language Examples; 2.9 Specialized Languages; 2.10 Bibliographical and Historical Remarks; Exercises; Chapter 3. Inference; 3.1 Derivability; 3.2 Inference Procedures; 3.3 Logical Implication.
- 3.4 Provability3.5 Proving Provability; 3.6 Bibliographical and Historical Remarks; Exercises; Chapter 4. Resolution; 4.1 Clausal Form; 4.2 Unification; 4.3 Resolution Principle; 4.4 Resolution; 4.5 Unsatisfiability; 4.6 True-or-False Questions; 4.7 Fill-in-the-Blank Questions; 4.8 Circuits Example; 4.9 Mathematics Example; 4.10 Soundness and Completeness; 4.11 Resolution and Equality; 4.12 Bibliographical and Historical Remarks; Exercises; Chapter 5. Resolution Strategies; 5.1 Deletion Strategies; 5.2 Unit Resolution; 5.3 Input Resolution; 5.4 Linear Resolution; 5.5 Set of Support Resolution.
- 5.6 Ordered Resolution5.7 Directed Resolution; 5.8 Sequential Constraint Satisfaction; 5.9 Bibliographical and Historical Remarks; Exercises; Chapter 6. Nonmonotonic Reasoning; 6.1 The Closed-World Assumption; 6.2 Predicate Completion; 6.3 Taxonomic Hierarchies and Default Reasoning; 6.4 Circumscription; 6.5 More General Forms of Circumscription; 6.6 Default Theories; 6.7 Bibliographical and Historical Remarks; Exercises; Chapter 7. Induction; 7.1 Induction; 7.2 Concept Formation; 7.3 Experiment Generation; 7.4 Bibliographical and Historical Remarks; Exercises.
- Chapter 8. Reasoning with Uncertain Beliefs8.1 Probabilities of Sentences; 8.2 Using Bayes' Rule in Uncertain Reasoning; 8.3 Uncertain Reasoning in Expert Systems; 8.4 Probabilistic Logic; 8.5 Probabilistic Entailment; 8.6 Computations Appropriate for Small Matrices; 8.7 Dealing with Large Matrices; 8.8 Probabilities Conditioned on Specific Information ; 8.9 Bibliographical and Historical Remarks; Exercises; Chapter 9. Knowledge and Belief; 9.1 Preliminaries; 9.2 Sentential Logics of Belief; 9.3 Proof Methods; 9.4 Nested Beliefs; 9.5 Quantifying-in; 9.6 Proof Methods for Quantified Beliefs.
- 9.7 Knowing What Something Is9.8 Possible-Worlds Logics; 9.9 Properties of Knowledge; 9.10 Properties of Belief; 9.11 Group Knowledge; 9.12 Equality, Quantification, and Knowledge; 9.13 Bibliographical and Historical Remarks; Exercises; Chapter 10. Metaknowledge and Metareasoning; 10.1 Metalanguage; 10.2 Clausal Form; 10.3 Resolution Principle; 10.4 Inference Procedures; 10.5 Derivability and Belief; 10.6 Metalevel Reasoning; 10.7 Bilevel Reasoning; 10.8 Reflection; 10.9 Bibliographical and Historical Remarks; Exercises; Chapter 11. State and Change; 11.1 States; 11.2 Actions.