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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. 02.0_pp_ii_iv_Reviews 04.0_pp_vi_vi_Copyright_page 05.0_pp_vii_viii_Dedication 06.0_pp_ix_xx_Contents 07.0_pp_xxi_xxvi_Preface 08.0_pp_1_76_Agents_in_the_World 08.1_pp_3_50_Artificial_Intelligence_and_Agents 08.2_pp_51_76_Agent_Architectures_and_Hierarchical_Control 09.0_pp_77_258_Reasoning_and_Planning_with_Certainty 09.1_pp_79_126_Searching_for_Solutions 09.2_pp_127_176_Reasoning_with_Constraints 09.3_pp_177_230_Propositions_and_Inference 09.4_pp_231_258_Deterministic_Planning 10.0_pp_259_514_Learning_and_Reasoning_with_Uncertainty 10.1_pp_261_326_Supervised_Machine_Learning 10.2_pp_327_374_Neural_Networks_and_Deep_Learning 10.3_pp_375_458_Reasoning_with_Uncertainty 10.4_pp_459_490_Learning_with_Uncertainty 10.5_pp_491_514_Causality 11.0_pp_515_642_Planning_and_Acting_with_Uncertainty 11.1_pp_517_582_Planning_with_Uncertainty 11.2_pp_583_608_Reinforcement_Learning 11.3_pp_609_642_Multiagent_Systems 12.0_pp_643_764_Representing_Individuals_and_Relations 12.1_pp_645_700_Individuals_and_Relations 12.2_pp_701_730_Knowledge_Graphs_and_Ontologies 12.3_pp_731_764_Relational_Learning_and_Probabilistic_Reasoning 13.0_pp_765_796_The_Big_Picture 13.1_pp_767_784_The_Social_Impact_of_Artificial_Intelligence 13.2_pp_785_796_Retrospect_and_Prospect 14.1_pp_797_802_Mathematical_Preliminaries_and_Notation 14.2_pp_803_806_Mapping_to_Open-Source_Packages 15.0_pp_807_842_References 16.0_pp_843_846_Index_of_Algorithms 17.0_pp_847_870_Index A comprehensive learning resource for undergraduate and graduate students, with new chapters on deep learning, causality, and social impact.