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

Subject Matter

 * Logic

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



Source work progress
* : Chapter $1$: Introduction