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Universal Artificial Intelligence

Name: Universal Artificial Intelligence
Brand: Springer, Berlin
SKU: 9783642060526
Price: 34513 HUF
Availability: InStock

Sequential Decisions Based on Algorithmic Probability

Szerző: Marcus Hutter

Nyelv:

Kötés: Puha kötésű

Kiadó: Springer, Berlin

Elérhetőség: Beszállítói készleten

Küldés 5-8 napon belül

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This book presents sequential decision theory from a novel algorithmic information theory perspectiv...

Információk a könyvről

Szerző

Marcus Hutter

Nyelv

Angol

Kötés

Könyv - Puha kötésű

Kiadva

2010

oldal

278

EAN

9783642060526

ISBN

3642060528

Enbook ID

01651347

Kiadó

Springer, Berlin

Súly

468

Méretek

155 x 16 x 16

Teljes leírás

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.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.