Patterson D 2005 Introduction To Artificial Intelligence And Expert Systems Prentice Hall Pdf


Download Patterson D 2005 Introduction To Artificial Intelligence And Expert Systems Prentice Hall Pdf PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Patterson D 2005 Introduction To Artificial Intelligence And Expert Systems Prentice Hall Pdf book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.

Download

The Quest for Artificial Intelligence


The Quest for Artificial Intelligence

Author: Nils J. Nilsson

language: en

Publisher: Cambridge University Press

Release Date: 2009-10-30


DOWNLOAD





Artificial intelligence (AI) is a field within computer science that is attempting to build enhanced intelligence into computer systems. This book traces the history of the subject, from the early dreams of eighteenth-century (and earlier) pioneers to the more successful work of today's AI engineers. AI is becoming more and more a part of everyone's life. The technology is already embedded in face-recognizing cameras, speech-recognition software, Internet search engines, and health-care robots, among other applications. The book's many diagrams and easy-to-understand descriptions of AI programs will help the casual reader gain an understanding of how these and other AI systems actually work. Its thorough (but unobtrusive) end-of-chapter notes containing citations to important source materials will be of great use to AI scholars and researchers. This book promises to be the definitive history of a field that has captivated the imaginations of scientists, philosophers, and writers for centuries.

Reinforcement Learning, second edition


Reinforcement Learning, second edition

Author: Richard S. Sutton

language: en

Publisher: MIT Press

Release Date: 2018-11-13


DOWNLOAD





The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.

Introduction to Artificial Intelligence and Expert Systems


Introduction to Artificial Intelligence and Expert Systems

Author: Dan W. Patterson

language: en

Publisher:

Release Date: 1990


DOWNLOAD