Fully revised and up to date, 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 up to date with the methods which have been proven to work. The book’s novel agent design space provides a coherent framework for learning, reasoning and decision making. A large number of realistic applications and examples facilitate student understanding. Each and every concept or set of rules 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 all the way through the book and connect the design approaches to the applications. Every chapter now has a social affect section, enabling students to take note the affect of the quite a lot of 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.
Artificial Intelligence: Foundations of Computational Agents
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