Book contents
- Frontmatter
- Dedication
- Contents
- Preface
- 1 Logic-Based Approach to Agent Design
- 2 Answer Set Prolog (ASP)
- 3 Roots of Answer Set Prolog
- 4 Creating a Knowledge Base
- 5 Representing Defaults
- 6 The Answer-Set Programming Paradigm
- 7 Algorithms for Computing Answer Sets
- 8 Modeling Dynamic Domains
- 9 Planning Agents
- 10 Diagnostic Agents
- 11 Probabilistic Reasoning
- 12 The Prolog Programming Language
- Appendix A ASP Solver Quick-Start
- Appendix B Aspide
- Appendix C Introduction to SPARC
- Appendix D Code
- Bibliography
- Index
Preface
Published online by Cambridge University Press: 05 July 2014
- Frontmatter
- Dedication
- Contents
- Preface
- 1 Logic-Based Approach to Agent Design
- 2 Answer Set Prolog (ASP)
- 3 Roots of Answer Set Prolog
- 4 Creating a Knowledge Base
- 5 Representing Defaults
- 6 The Answer-Set Programming Paradigm
- 7 Algorithms for Computing Answer Sets
- 8 Modeling Dynamic Domains
- 9 Planning Agents
- 10 Diagnostic Agents
- 11 Probabilistic Reasoning
- 12 The Prolog Programming Language
- Appendix A ASP Solver Quick-Start
- Appendix B Aspide
- Appendix C Introduction to SPARC
- Appendix D Code
- Bibliography
- Index
Summary
This is a book about knowledge representation and reasoning (KRR) — a comparatively new branch of science that serves as the foundation of artificial intelligence, declarative programming, and the design of intelligent agents — knowledge-intensive software systems capable of exhibiting intelligent behavior. Our main goal is to show how a software system can be given knowledge about the world and itself and how this knowledge can be used to solve nontrivial computational problems. There are several approaches to KRR that both compete with and complement each other. The approaches differ primarily by the languages used to represent knowledge and by corresponding computational methods. This book is based on a knowledge representation language called Answer Set Prolog (ASP) and the answer-set programming paradigm — a comparatively recent branch of KRR with a well-developed theory, efficient reasoning systems, methodology of use, and a growing number of applications.
The text can be used for classes in knowledge representation, declarative programming, and artificial intelligence for advanced undergraduate or graduate students in computer science and related disciplines, including software engineering, logic, and cognitive science. It will also be useful to serious researchers in these fields who would like to learn more about the answer-set programming paradigm and its use for KRR. Finally, we hope that it will be of interest to anyone with a sense of wonder about the amazing ability of humans to derive volumes of knowledge from a collection of basic facts.
- Type
- Chapter
- Information
- Knowledge Representation, Reasoning, and the Design of Intelligent AgentsThe Answer-Set Programming Approach, pp. xi - xivPublisher: Cambridge University PressPrint publication year: 2014