Artificial intelligence : foundations of computational agents / David L. Poole, Alan K. Mackworth.
Material type: TextPublisher: Cambridge : Cambridge University Press, 2017Edition: Second editionDescription: xxviii, 792 pages : illustrations ; 27 cmContent type:- text
- unmediated
- 9781107195394
- 006.3 PO.A 2017 23
- Q342 .P66 2017
Item type | Current library | Collection | Call number | Status | Date due | Barcode | |
---|---|---|---|---|---|---|---|
Books | The Knowledge Hub Library | Computing | 006.3 PO.A 2017 (Browse shelf(Opens below)) | Available | 190259 |
"4th printing 2019"-- title page verso.
Includes bibliographical references (pages 751-771) and index.
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.
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.
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