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Logical Foundations of Artificial Intelligence-[1St Edition]-[1987]-[djvu]-[Michael R. Genesereth, Nils J. Nilsson]

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书籍信息:
书名: Logical Foundations of Artificial Intelligence
语言: English
格式: djvu
大小: 3.0M
页数: 406
年份: 1987
作者: Michael R. Genesereth, Nils J. Nilsson
版次: 1St Edition
出版社: Morgan Kaufmann

简介

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 of logic.

The first section of the book introduces the logicist approach to AI--discussing the representation of declarative knowledge and featuring an introduction to the process of conceptualization, the syntax and semantics of predicate calculus, and the basics of other declarative representations such as frames and semantic nets.  This section also provides a simple but powerful inference procedure, resolution, and shows how it can be used in a reasoning system.

The next several chapters discuss nonmonotonic reasoning, induction, and reasoning under uncertainty, broadening the logical approach to deal with the inadequacies of strict logical deduction.  The third section introduces modal operators that facilitate representing and reasoning about knowledge.  This section also develops the process of writing predicate calculus sentences to the metalevel--to permit sentences about sentences and about reasoning processes.  The final three chapters discuss the representation of knowledge about states and actions, planning, and intelligent system architecture.

End-of-chapter bibliographic and historical comments provide background and point to other works of interest and research.  Each chapter also contains numerous student exercises (with solutions provided in an appendix) to reinforce concepts and challenge the learner.  A bibliography and index complete this comprehensive work


目录
Content: Typographical Conventions  1 Introduction  1.1 Bibliographical and Historical Remarks  Exercises  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  3 Inference  3.1 Derivability  3.2 Inference Procedures  3.3 Logical Implication  3.4 Provability  3.5 Proving Provability  3.6 Bibliographical and Historical Remarks  Exercises  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  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 Resolution  5.7 Directed Resolution  5.8 Sequential Constraint Satisfaction  5.9 Bibliographical and Historical Remarks  Exercises  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  7 Induction  7.1 Induction  7.2 Concept Formation  7.3 Experiment Generation  7.4 Bibliographical and Historical Remarks  Exercises  8 Reasoning with Uncertain Beliefs  8.1 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  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 Is  9.8 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  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  11 State and Change  11.1 States  11.2 Actions  11.3 The Frame Problem  11.4 Action Ordering  11.5 Conditionality  11.6 Bibliographical and Historical Remarks  Exercises  12 Planning  12.1 Initial State  12.2 Goals  12.3 Actions  12.4 Plans  12.5 Green's Method  12.6 Action Blocks  12.7 Conditional Plans  12.8 Planning Direction  12.9 Unachievability Pruning  12.10 State Alignment  12.11 Frame-Axiom Suppression  12.12 Goal Regression  12.13 State Differences  12.14 Bibliographical and Historical Remarks  Exercises  13 Intelligent-Agent Architecture  13.1 Tropistic Agents  13.2 Hysteretic Agents  13.3 Knowledge-Level Agents  13.4 Stepped Knowledge-Level Agents  13.5 Fidelity  13.6 Deliberate Agents  13.7 Bibliographical and Historical Remarks  Exercises  Answers to Exercises  A.1 Introduction  A.2 Declarative Knowledge  A.3 Inference  A.4 Resolution  A.5 Resolution Strategies  A.6 Nonmonotonic Reasoning  A.7 Induction  A.8 Reasoning with Uncertain Beliefs  A.9 Knowledge and Belief  A.10 Metaknowledge and Metareasoning  A.11 State and Change  A.12 Planning  A.13 Intelligent-Agent Architecture References  Index

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