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Artificial intelligence, including machine learning, has emerged as a transformational science and engineering discipline. Artificial Intelligence: Foundations of Computational Agents presents AI using a coherent framework to study the design of intelligent computational agents. By showing how the basic approaches fit into a multidimensional design space, readers learn the fundamentals without losing sight of the bigger picture. The new edition also features expanded coverage on machine learning material, as well as on the social and ethical consequences of AI and ML. The book balances theory and experiment, showing how to link them together, and develops the science of AI together with its engineering applications. Although structured as an undergraduate and graduate textbook, the book's straightforward, self-contained style will also appeal to an audience of professionals, researchers, and independent learners. The second edition is well-supported by strong pedagogical features and online resources to enhance student comprehension.
List of contents
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. Reasoning, Planning and Learning with Certainty: 3. Searching for solutions; 4. Reasoning with constraints; 5. Propositions and inference; 6. Planning with certainty; 7. Supervised machine learning; Part III. Reasoning, Learning and Acting with Uncertainty: 8. Reasoning with uncertainty; 9. Planning with uncertainty; 10. Learning with uncertainty; 11. Multiagent systems; 12. Learning to act; Part IV. Reasoning, Learning and Acting with Individuals and Relations: 13. Individuals and relations; 14. Ontologies and knowledge-based systems; 15. Relational planning, learning, and probabilistic reasoning; Part V. Retrospect and Prospect: 16. Retrospect and prospect; Part VI. End Matter: Appendix A. Mathematical preliminaries and notation.
Summary
The second edition of Artificial Intelligence provides a comprehensible and thorough guide for junior to senior undergraduates and graduate students. Updates include a major expansion on machine learning, inclusion of more intuitive explanations and exercises, and simplification of parts that were considered difficult in the first edition.
Report
'This new edition of the already classic Introduction to Artificial Intelligence by Poole and Mackworth provides a broad coverage of the symbolic and non-symbolic approaches underpinning the main current and future approaches to AI. It combines an accessible treatment of the underlying theory with practical examples that bring the theory to life. It is essential reading for anyone who wants to understand the current state of the art, and to be prepared for the future.' Robert Kowalski, Imperial College London
