Universal Artificial Intelligence: Sequential Decisions Based on Algorithmic Probability - Texts in Theoretical Computer Science: an Eatcs Series - Marcus Hutter - Books - Springer-Verlag Berlin and Heidelberg Gm - 9783642060526 - November 6, 2010
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Marcus Hutter
Universal Artificial Intelligence: Sequential Decisions Based on Algorithmic Probability - Texts in Theoretical Computer Science: an Eatcs Series Softcover Reprint of Hardcover 1st Ed. 2005 edition

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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 different ideas and removes the limitations by unifying them to one parameter-free theory of an optimal reinforcement learning agent embedded in an unknown environment. Most AI problems can easily be formulated within this theory, reducing 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. 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.

298 pages, black & white illustrations

Media Books     Paperback Book   (Book with soft cover and glued back)
Released November 6, 2010
ISBN13 9783642060526
Publishers Springer-Verlag Berlin and Heidelberg Gm
Pages 298
Dimensions 155 × 235 × 16 mm   ·   426 g
Language English  

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