Neuware - 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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Detalles bibliogr�ficos

T�tulo: Artificial Intelligence : Foundations of Computational Agents
Editor: Cambridge University Pr. Okt 2023
Fecha de publicaci�n: 2023
Idioma: Ingl�s
ISBN 10: 1009258192
ISBN 13: 9781009258197
Encuadernaci�n: Buch
Edici�n: 3� Edici�n
Estado: Neu

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Sinopsis

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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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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