Artificial Intelligence: Foundations of Computational Agents 3/e [Poole] 9781009258197

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.

頁數：896
版次：第3版
年份：2023年
規格：精裝/單色
ISBN：9781009258197

Part I - Agents in the World
Chapter 1 - Artificial Intelligence and Agents
Chapter 2 - Agent Architectures and Hierarchical Control
Part II - Reasoning and Planning with Certainty
Chapter 3 - Searching for Solutions
Chapter 4 - Reasoning with Constraints
Chapter 5 - Propositions and Inference
Chapter 6 - Deterministic Planning
Part III - Learning and Reasoning with Uncertainty
Chapter 7 - Supervised Machine Learning
Chapter 8 - Neural Networks and Deep Learning
Chapter 9 - Reasoning with Uncertainty
Chapter 10 - Learning with Uncertainty
Chapter 11 - Causality
Part IV - Planning and Acting with Uncertainty
Chapter 12 - Planning with Uncertainty
Chapter 13 - Reinforcement Learning
Chapter 14 - Multiagent Systems
Part V - Representing Individuals and Relations
Chapter 15 - Individuals and Relations
Chapter 16 - Knowledge Graphs and Ontologies
Chapter 17 - Relational Learning and Probabilistic Reasoning
Part VI - The Big Picture
Chapter 18 - The Social Impact of Artificial Intelligence
Chapter 19 - Retrospect and Prospect
Appendix A - Mathematical Preliminaries and Notation
Appendix B - Mapping to Open-Source Packages