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.
