Universal Bayesian Agents: Theory and Applications
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If you have a question about this talk, please contact Zoubin Ghahramani.
The dream of creating artificial devices that reach or
outperform human intelligence is many centuries old. In this
talk I present an elegant parameter-free theory of an optimal
reinforcement learning agent embedded in an arbitrary unknown
environment that possesses essentially all aspects of rational
intelligence. The theory reduces all conceptual AI problems to
pure computational questions. The necessary and sufficient
ingredients are Bayesian probability theory; algorithmic
information theory; universal Turing machines; the agent
framework; sequential decision theory; and reinforcement
learning, which are all important subjects in their own right.
I also present some recent approximations, implementations, and
applications of this modern top-down approach to AI.
This talk is part of the Machine Learning @ CUED series.
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