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An Introduction to Universal Artificial Intelligence provides the formal underpinning of what it means for an agent to act intelligently in an unknown environment. First presented in Universal Algorithmic Intelligence (Hutter, 2000), UAI offers a framework in which virtually all AI problems can be formulated, and a theory of how to solve them. UAI unifies ideas from sequential decision theory, Bayesian inference, and algorithmic information theory to construct AIXI, an optimal reinforcement learning agent that learns to act optimally in unknown environments. AIXI is the theoretical gold standard for intelligent behavior.
The book covers both the theoretical and practical aspects of UAI. Bayesian updating can be done efficiently with context tree weighting, and planning can be approximated by sampling with Monte Carlo tree search. It provides algorithms for the reader to implement, and experimental results to compare against. These algorithms are used to approximate AIXI. The book ends with a philosophical discussion of Artificial General Intelligence: Can super-intelligent agents even be constructed? Is it inevitable that they will be constructed, and what are the potential consequences?
This text is suitable for late undergraduate students. It provides an extensive chapter to fill in the required mathematics, probability, information, and computability theory background. You can also visit the author website: http://www.hutter1.net/ai/uaibook2.htm.
ASIN : B0CW19YSQ3
Publisher : Chapman and Hall/CRC
Accessibility : Learn more
Publication date : May 28, 2024
Edition : 1st
Language : English
File size : 25.3 MB
Simultaneous device usage : Up to 4 simultaneous devices, per publisher limits
Enhanced typesetting : Not Enabled
X-Ray : Not Enabled
Word Wise : Not Enabled
Format : Print Replica
ISBN-13 : 978-1003821977
Page Flip : Not Enabled
Part of series : Chapman & Hall/CRC Artificial Intelligence and Robotics
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