Knowledge Representation and Reasoning The Morgan Kaufmann Series in Artificial Intelligence 1st Edition by Ronald Brachman, Hector Levesque ISBN 1558609326 9781558609327

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ISBN 10: 1558609326
ISBN 13: 9781558609327
Author: Ronald Brachman, Hector Levesque
Knowledge 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 lucid and compelling way. Each of the various styles of representation is presented in a simple and intuitive form, and the basics of reasoning with that representation are explained in detail. This approach gives readers a solid foundation for understanding the more advanced work found in the research literature. The presentation is clear enough to be accessible to a broad audience, including researchers and practitioners in database management, information retrieval, and object-oriented systems as well as artificial intelligence. This book provides the foundation in knowledge representation and reasoning that every AI practitioner needs.

Knowledge Representation and Reasoning The Morgan Kaufmann Series in Artificial Intelligence 1st Edition Table of contents:

Part I: Introduction to Knowledge Representation

1. Introduction to Knowledge Representation

   • What is Knowledge Representation?
   • The Role of Knowledge in Intelligent Systems
   • Approaches to Knowledge Representation in AI
   • Types of Knowledge: Propositional, Relational, Procedural

2. Knowledge Representation and Reasoning

   • The Relationship Between Knowledge Representation and Reasoning
   • Deductive and Non-deductive Reasoning
   • The Role of Formal Logic in KR
   • Reasoning as Search and Inference

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Part II: Representational Models and Frameworks

3. Logical Foundations of Knowledge Representation

   • Classical Logic and its Role in KR
   • Propositional Logic
   • Predicate Logic: Syntax and Semantics
   • Beyond Classical Logic: Modal and Non-monotonic Logics

4. Semantic Networks and Frames

   • The Concept of Semantic Networks
   • Frames as a Knowledge Representation Formalism
   • Inheritance and Categorization in Frames
   • Applications of Semantic Networks and Frames

5. Description Logics

   • Introduction to Description Logics (DL)
   • Syntax and Semantics of Description Logics
   • Reasoning with DL
   • Applications in Ontologies and Knowledge Graphs

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Part III: Reasoning Techniques

6. Inference and Deductive Reasoning

   • The Basics of Deductive Inference
   • The Tableau Method
   • Resolution and Unification
   • Soundness and Completeness of Inference Systems

7. Non-monotonic Reasoning

   • The Problem of Non-monotonicity
   • Default Reasoning
   • Circumscription and Autoepistemic Logic
   • Handling Uncertainty and Inconsistency in Reasoning

8. Probabilistic Reasoning

   • Introduction to Probability Theory
   • Bayesian Networks and Inference
   • Probabilistic Logic and Decision Making
   • Applications of Probabilistic Reasoning in AI

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Part IV: Knowledge Representation in Practice

9. Reasoning about Actions and Change

   • The Frame Problem and Solutions
   • Representing Actions in Temporal Logics
   • Planning and Goal-Directed Behavior
   • Dynamic Systems and Change

10. Commonsense Reasoning

    • The Challenge of Commonsense Knowledge
    • Commonsense Reasoning Systems
    • Representing Temporal and Spatial Knowledge
    • Applications in Natural Language Understanding

11. Knowledge Representation for Natural Language Processing

    • Linking Language and Knowledge Representation
    • Representing Meaning in Natural Language
    • Lexical Semantics and Syntax
    • Applications in Machine Translation and Text Understanding

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Part V: Applications and Advanced Topics

12. Knowledge Representation in Expert Systems

    • Building Knowledge-Based Systems
    • The Role of KR in Expert Systems
    • Knowledge Acquisition and Representation Techniques
    • Inference in Expert Systems

13. Knowledge Graphs and Ontologies

    • The Role of Ontologies in Knowledge Representation
    • Building and Querying Knowledge Graphs
    • Applications of Ontologies in the Semantic Web
    • Managing Large-Scale Knowledge Repositories

14. Learning and Knowledge Representation

    • Machine Learning and Knowledge Representation
    • Integrating KR with Learning Algorithms
    • Reasoning with Learned Knowledge
    • Applications of KR in Robotics and Autonomous Systems

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Conclusion
15. The Future of Knowledge Representation and Reasoning
– Challenges in Knowledge Representation
– The Intersection of KR and Artificial Intelligence
– Future Directions for Research and Applications

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