Book:Michael R. Genesereth/Logical Foundations of Artificial Intelligence

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Michael R. Genesereth and Nils J. Nilsson: Logical Foundations of Artificial Intelligence

Published $1987$, Morgan Kaufman

ISBN 0-934613-31-1.


Subject Matter


Contents

Acknowledgments
Preface
Typographical Conventions
1 Introduction
1.1 Bibliographical and Historical Remarks
Exercises
2 Declarative Knowledge
2.1 Conceptualisation
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