Home Universal Artificial Intelligence: Sequential Decisions Based on Algorithmic Probability by Marcus Hutter (ISBN: 9783540221395)

Stock photo: cover may vary

Universal Artificial Intelligence: Sequential Decisions Based on Algorithmic Probability Hardback - 2004 - 2005th Edition

Name: Universal Artificial Intelligence: Sequential Decisions Based on Algorithmic Probability
Price: 60.67 GBP
Availability: InStock
Author: Hutter, Marcus
ISBN: 9783540221395

by Marcus Hutter

Add to wish list

Browse more copies for sale ↓

Reader reviews for Universal Artificial Intelligence: Sequential Decisions Based on Algorithmic Probability

Write a review for this book

Important Terms and Guidelines

Please focus on the book’s content and context. Also, add any personal comments as to how you enjoyed the book. Substantiate your likes and dislikes. You may make comparisons to other books.
Reviews must be at least 140 characters in length.
Please do not reveal critical plot elements.
This is not a help line. Contact customer support if you need help.

Your review must not include:

Obscenities, discriminatory language, or other insulting language not suitable for public domain
Advertisements, “spam” content, or references to other products, offers or websites.
Email addresses, URLs, phone numbers, physical addresses or other contact information.
Overly critical comments about other reviews or reviewers
Time-sensitive material (i.e. promotional tours, seminars, lectures, etc.)
Availability, price, or alternative ordering/shipping information

From the publisher

This book presents sequential decision theory from a novel algorithmic information theory perspective. While the former is suited for active agents in known environment, the latter is suited for passive prediction in unknown environment. 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.

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.

Details

Title Universal Artificial Intelligence: Sequential Decisions Based on Algorithmic Probability
Author Marcus Hutter
Binding Hardback
Edition number 2005th
Edition 2005
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
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)
Category Computers - General Information
Library of Congress Catalogue Number 2004112980
Dewey Decimal Code 006.3

About the author

Marcus Hutter received his masters in computer sciences in 1992 at the Technical University in Munich, Germany. After his PhD in theoretical particle physics he developed algorithms in a medical software company for 5 years. For four years he has been working as a researcher at the AI institute IDSIA in Lugano, Switzerland. His current interests are centered around reinforcement learning, algorithmic information theory and statistics, universal induction schemes, adaptive control theory, and related areas.

IDSIA (Istituto Dalle Molle di Studi sull'Intelligenza Artificiale) is a non-profit oriented research institute for artificial intelligence, affiliated with both the University of Lugano and SUPSI. It focusses on machine learning (artificial neural networks, reinforcement learning), optimal universal artificial intelligence and optimal rational agents, operations research, complexity theory, and robotics. In Business Week's "X-Lab Survey" IDSIA was ranked in fourth place in the category "Computer Science - Biologically Inspired", after much larger institutions. IDSIA also ranked in the top 10 of the broader category "Artificial Intelligence."

More Copies for Sale

Stock photo: cover may vary

Universal Artificial Intelligence: Sequential Decisions Based on Algorithmic Probability

by Hutter, Marcus

Used
Hardback

Condition: Used
Binding: Hardcover
ISBN 10 / ISBN 13: 9783540221395 / 3540221395
Quantity available: 1
Seller: Anybook.com
Item price: A$117.80

A$26.89 Delivery to USA

Show details

Description:

Springer, 2004. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In fair condition, suitable as a study copy. No dust jacket. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,700grams, ISBN:9783540221395