Artificial intelligence
Subtitle: foundations of computational agents
By: David L. Poole, Alan K. Mackworth
Contributors: Mackworth, Alan K.
Publisher: Cambridge University Press
Published: 2010
Place of publication: New York
Language: eng
Number of pages: 662
ISBN-13: 9780521519007
Description: "Recent decades have witnessed the emergence of artificial intelligence as a serious science and engineering discipline. Artificial Intelligence: Foundations of Computational Agents is a textbook aimed at junior to senior undergraduate students and first-year graduate students. It presents artificial intelligence (AI) using a coherent framework to study the design of intelligent computational agents. By showing how basic approaches fit into a multidimensional design space, readers can learn the fundamentals without losing sight of the bigger picture. The book balances theory and experiment, showing how to link them intimately together, and develops the science of AI together with its engineering applications. Although structured as a textbook, the book's straightforward, self-contained style will also appeal to a wide audience of professionals, researchers, and independent learners. AI is a rapidly developing field: this book encapsulates the latest results without being exhaustive and encyclopedic. It teaches the main principles and tools that will allow readers to explore and learn on their own. The text is supported by an online learning environment, AIspace, http://aispace.org, so that students can experiment with the main AI algorithms plus problems, animations, lecture slides, and a knowledge representation system, AIlog, for experimentation and problem solving"--Provided by publisher.
Notes: Includes bibliographical references and index.
Table of contents: title: Machine generated contents note: Part I. Agents in the World: What Are Agents and How Can They Be Built?: 1. Artificial intelligence and agents; 2. Agent architectures and hierarchical control; Part II. Representing and Reasoning: 3. States and searching; 4. Features and constraints; 5. Propositions and inference; 6. Reasoning under uncertainty; Part III. Learning and Planning: 7. Learning: overview and supervised learning; 8. Planning with certainty; 9. Planning under uncertainty; 10. Multiagent systems; 11. Beyond supervised learning; Part IV. Reasoning about Individuals and Relations: 12. Individuals and relations; 13. Ontologies and knowledge-based systems; 14. Relational planning, learning and probabilistic reasoning; Part V. The Big Picture: 15. Retrospect and prospect; Appendix A. Mathematical preliminaries and notation.