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Universal Artificial Intelligence: Sequential Decisions Based On Algorithmic Probability Hardback - 2004 - 2005th Edition
by Hutter, Marcus
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- Hardback
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Details
- Title Universal Artificial Intelligence: Sequential Decisions Based On Algorithmic Probability
- Author Hutter, Marcus
- Binding Hardback
- Edition number 2005th
- Edition 2005
- Condition Used: Good
- Pages 278
- Volumes 1
- Language ENG
- Publisher Springer, Secaucus, New Jersey, U.S.A.
- Publication date 2004-10-12
- Illustrated Yes
- Features Bibliography, Illustrated, Index, Table of Contents
- Bookseller's Inventory # SONG3540221395
- ISBN 9783540221395 / 3540221395
- Weight 1.43 lbs (0.65 kg)
- Dimensions 9.46 x 6.54 x 0.8 in (24.03 x 16.61 x 2.03 cm)
- Size 6.54x0.80x9.46
- Category Computers - General Information
- Library of Congress Catalogue Number 2004112980
- Dewey Decimal Code 006.3
- Quantity available 1
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From the publisher
First line
HASH(0x11135460)
From the rear cover
Decision Theory = Probability + Utility Theory
+ +
Universal Induction = Ockham + Bayes + Turing
= =
A Unified View of Artificial Intelligence
This book presents sequential decision theory from a novel algorithmic information theory perspective. While the former is suited for active agents in known environments, the latter is suited for passive prediction in unknown environments.
The book introduces these two well-known but very different ideas and removes the limitations by unifying them to one parameter-free theory of an optimal reinforcement learning agent embedded in an arbitrary unknown environment. Most if not all AI problems can easily be formulated within this theory, which reduces the conceptual problems to pure computational ones. Considered problem classes include sequence prediction, strategic games, function minimization, reinforcement and supervised learning. The discussion includes formal definitions of intelligence order relations, the horizon problem and relations to other approaches to AI. One intention of this book is to excite a broader AI audience about abstract algorithmic information theory concepts, and conversely to inform theorists about exciting applications to AI.
