Artificial Intelligence: Foundations of Computational Agents (3版)

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【簡介】 Introduces concepts at the right level of detail, using straightforward language and minimum mathematics Examines the social and ethical impacts of AI, connecting techniques to real-world benefits and harms Explains state-of-the-art algorithms and examples underlying the theory, demonstrating how concepts are applied Provides two complementary software systems – AIPython and AILog - for experimentation and extension Pedagogical features include examples, end-of-chapter reviews, further reading lists, and exercises 【目錄】 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