Artificial intelligence : foundations of computational agents

著者

書誌事項

Artificial intelligence : foundations of computational agents

David L. Poole, Alan K. Mackworth

Cambridge University Press, 2010

  • : hbk

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注記

Includes bibliographical references (p.637-652) and index

内容説明・目次

内容説明

Recent decades have witnessed the emergence of artificial intelligence as a serious science and engineering discipline. This textbook, aimed at junior to senior undergraduate students and first-year graduate students, 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. 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.

目次

  • 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.

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