Artificial Intelligence
Foundations of Computational Agents

A comprehensive learning resource for undergraduate and graduate students, with new chapters on deep learning, causality, and social impact.

David L. Poole (Author), Alan K. Mackworth (Author)

9781009258197, Cambridge University Press

Hardback, published 13 July 2023

900 pages
26 x 18.2 x 4.3 cm, 1.97 kg

'Poole and Mackworth's Artificial Intelligence: Foundations of Computational Agents 3e is a tour de force. This is a comprehensive and clearly written text that takes the reader through core concepts in symbolic AI and machine learning, providing pathways for broad introductory undergraduate courses, or focused graduate courses. It's an outstanding resource for student and instructor alike. Whether you're a seasoned AI researcher or a student entering the field, you'll learn a great deal from reading this book.' Sheila McIlraith, University of Toronto

Fully revised and updated, this third edition includes three new chapters on neural networks and deep learning including generative AI, causality, and the social, ethical and regulatory impacts of artificial intelligence. All parts have been updated with the methods that have been proven to work. The book's novel agent design space provides a coherent framework for learning, reasoning and decision making. Numerous realistic applications and examples facilitate student understanding. Every concept or algorithm is presented in pseudocode and open source AIPython code, enabling students to experiment with and build on the implementations. Five larger case studies are developed throughout the book and connect the design approaches to the applications. Each chapter now has a social impact section, enabling students to understand the impact of the various techniques as they learn them. An invaluable teaching package for undergraduate and graduate AI courses, this comprehensive textbook is accompanied by lecture slides, solutions, and code.

Preface
Part I. Agents in the World: 1. Artificial intelligence and agents
2. Agent architectures and hierarchical control
Part II. Reasoning and Planning with Certainty: 3. Searching for solutions
4. Reasoning with constraints
5. Propositions and inference
6. Deterministic planning
Part III. Learning and Reasoning with Uncertainty: 7. Supervised machine learning
8. Neural networks and deep learning
9. Reasoning with uncertainty
10. Learning with uncertainty
11. Causality
Part IV. Planning and Acting with Uncertainty
12. Planning with uncertainty
13. Reinforcement learning
14. Multiagent systems
Part V. Representing Individuals and Relations: 15. Individuals and relations
16. Knowledge graphs and ontologies
17. Relational learning and probabilistic reasoning
Part VI. The Big Picture: 18. The social impact of artificial intelligence
19. Retrospect and prospect
Appendices
References
Index of Algorithms
Index.

Subject Areas: Artificial intelligence [UYQ]