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
Reviews & endorsements
'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 LondonSee more reviews
'Poole and Mackworth provide a crystal clear introduction to artificial intelligence. Their book paints a complete picture of the field, from the logical foundations to the latest breakthroughs in learning, representation, reasoning, and multi-agent systems. The authors view AI as the integration of a diverse set of technologies. Layer by layer, they introduce all the techniques required to build an intelligent agent. The book stands out as being both comprehensive and uncompromising, limiting the material to the most promising and intellectually gratifying topics.' Guy Van den Broeck, University of California, Los Angeles
22nd Mar 2018 by Napoleonboakye1
Wow, I love this book. I read the 2010 version online and immediately recommended it to my students and scholars who were taking Ng's ML course on Coursera. It widens your horizon in terms of intelligence. It has been broken down to a layman's understanding and a researcher's intuitiveness. The way artificial intelligence and natural intelligence was explained was best to none. This newer version comes with brevity and conciseness. A must read even if you are only interested in ML.
Review was not posted due to profanity×
- 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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