ENGLISH

Universal Artificial Intelligence: Sequential Decisions Based On Algorithmic Probability

By Marcus Hutter

Book information

Publisher
Springer
Year
2005
ISBN
3540221395, 9783540221395
Language
english
Format
PDF
Filesize
18 MB (18373511 bytes)
Series
Texts in Theoretical Computer Science. An EATCS Series
Pages
301\301
Orientation
portrait
Paginated
yes
Scanned
yes
Time added
2012-03-17 06:00:00

Description

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.

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