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An Introduction to Universal Artificial Intelligence provides a
gentle introduction to Universal Artificial Intelligence (UAI), a
theory that provides a formal underpinning of what it means for an
agent to act intelligently in a general class of environments.
First presented in Universal Artificial Intelligence (Hutter,
2004), UAI presents a model in which most other problems in AI can
be presented, and unifies ideas from sequential decision theory,
Bayesian inference and information theory to construct AIXI, an
optimal reinforcement learning agent that learns to act optimally
in unknown environments. AIXI represents a theoretical bound on
intelligent behaviour, and so we also discuss tractable
approximations of this optimal agent. The book covers important
practical approaches including efficient Bayesian updating with
context tree weighting, and stochastic planning, approximated by
sampling with Monte Carlo tree search. Algorithms are also included
for the reader to implement, along with experimental results to
compare against. This serves to approximate AIXI, as well as being
used in state-of-the-art approaches in AI today. The book ends with
a philosophical discussion of AGI covering the following key
questions: Should intelligent agents be constructed at all, is it
inevitable that they will be constructed, and is it dangerous to do
so? This text is suitable for late undergraduates and includes an
extensive background chapter to fill in the assumed mathematical
background.
An Introduction to Universal Artificial Intelligence provides a
gentle introduction to Universal Artificial Intelligence (UAI), a
theory that provides a formal underpinning of what it means for an
agent to act intelligently in a general class of environments.
First presented in Universal Artificial Intelligence (Hutter,
2004), UAI presents a model in which most other problems in AI can
be presented, and unifies ideas from sequential decision theory,
Bayesian inference and information theory to construct AIXI, an
optimal reinforcement learning agent that learns to act optimally
in unknown environments. AIXI represents a theoretical bound on
intelligent behaviour, and so we also discuss tractable
approximations of this optimal agent. The book covers important
practical approaches including efficient Bayesian updating with
context tree weighting, and stochastic planning, approximated by
sampling with Monte Carlo tree search. Algorithms are also included
for the reader to implement, along with experimental results to
compare against. This serves to approximate AIXI, as well as being
used in state-of-the-art approaches in AI today. The book ends with
a philosophical discussion of AGI covering the following key
questions: Should intelligent agents be constructed at all, is it
inevitable that they will be constructed, and is it dangerous to do
so? This text is suitable for late undergraduates and includes an
extensive background chapter to fill in the assumed mathematical
background.
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