Automated Planning and Acting

Automated Planning and Acting
Title Automated Planning and Acting PDF eBook
Author Malik Ghallab
Publisher Cambridge University Press
Pages 373
Release 2016-08-09
Genre Computers
ISBN 1107037271

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This book presents the most recent and advanced techniques for creating autonomous AI systems capable of planning and acting effectively.

Automated Planning

Automated Planning
Title Automated Planning PDF eBook
Author Malik Ghallab
Publisher Elsevier
Pages 665
Release 2004-05-03
Genre Business & Economics
ISBN 1558608567

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Publisher Description

Artificial Intelligence

Artificial Intelligence
Title Artificial Intelligence PDF eBook
Author Stuart Russell
Publisher Createspace Independent Publishing Platform
Pages 626
Release 2016-09-10
Genre
ISBN 9781537600314

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Artificial Intelligence: A Modern Approach offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence.

A Concise Introduction to Models and Methods for Automated Planning

A Concise Introduction to Models and Methods for Automated Planning
Title A Concise Introduction to Models and Methods for Automated Planning PDF eBook
Author Hector Radanovic
Publisher Springer Nature
Pages 132
Release 2022-05-31
Genre Computers
ISBN 3031015649

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Planning is the model-based approach to autonomous behavior where the agent behavior is derived automatically from a model of the actions, sensors, and goals. The main challenges in planning are computational as all models, whether featuring uncertainty and feedback or not, are intractable in the worst case when represented in compact form. In this book, we look at a variety of models used in AI planning, and at the methods that have been developed for solving them. The goal is to provide a modern and coherent view of planning that is precise, concise, and mostly self-contained, without being shallow. For this, we make no attempt at covering the whole variety of planning approaches, ideas, and applications, and focus on the essentials. The target audience of the book are students and researchers interested in autonomous behavior and planning from an AI, engineering, or cognitive science perspective. Table of Contents: Preface / Planning and Autonomous Behavior / Classical Planning: Full Information and Deterministic Actions / Classical Planning: Variations and Extensions / Beyond Classical Planning: Transformations / Planning with Sensing: Logical Models / MDP Planning: Stochastic Actions and Full Feedback / POMDP Planning: Stochastic Actions and Partial Feedback / Discussion / Bibliography / Author's Biography

Reinforcement Learning, second edition

Reinforcement Learning, second edition
Title Reinforcement Learning, second edition PDF eBook
Author Richard S. Sutton
Publisher MIT Press
Pages 549
Release 2018-11-13
Genre Computers
ISBN 0262352702

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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.

New Directions in AI Planning

New Directions in AI Planning
Title New Directions in AI Planning PDF eBook
Author Malik Ghallab
Publisher
Pages 422
Release 1996
Genre Artificial intelligence
ISBN 9784274900648

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Human Compatible

Human Compatible
Title Human Compatible PDF eBook
Author Stuart Jonathan Russell
Publisher Penguin Books
Pages 354
Release 2019
Genre Business & Economics
ISBN 0525558616

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A leading artificial intelligence researcher lays out a new approach to AI that will enable people to coexist successfully with increasingly intelligent machines.