Foundations of Computational Agents
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- David L. Poole, University of British Columbia, Vancouver
- Alan K. Mackworth, University of British Columbia, Vancouver
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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.Read more
- Rich online supplementary resources include comprehensive Python code, problems, animations and lecture slides
- Provides three complementary software systems - Python, AILog and AIspace - for experimentation and extension
- Pedagogical features include examples, bolded key terms, end-of-chapter reviews, further reading lists and exercises
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- Edition: 2nd Edition
- Date Published: September 2017
- format: Adobe eBook Reader
- isbn: 9781108173773
- availability: This ISBN is for an eBook version which is distributed on our behalf by a third party.
Table 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.
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