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Synopsis
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.
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About the Authors
David L. Poole is Professor of Computer Science at the University of British Columbia. He is a former chair of the Association for Uncertainty in Artificial Intelligence, the winner of the Canadian AI Association (CAIAC) Lifetime Achievement Award, and is a Fellow of AAAI and CAIAC.
Alan K. Mackworth is a Professor Emeritus of Computer Science at the University of British Columbia, where he co-founded the pioneering UBC Cognitive Systems Program. He served as President of CAIAC, IJCAII, and AAAI, and now acts as a consultant, writer and lecturer. He is a Fellow of AAAI, CAIAC, CIFAR, AGE-WELL and the Royal Society of Canada.
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Artificial Intelligence: Foundations of Computational Agents
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Artificial Intelligence Foundations of Computational Agents
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Hardcover. Condition: new. Hardcover. 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. Fully revised and updated, this comprehensive new edition covers modern AI and machine learning for undergraduate and graduate students. Includes new chapters on deep learning including generative AI, causality and social impact, new social impact sections, major revisions to knowledge graphs, reasoning and decision making, and more AIPython code. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781009258197
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