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Information-Theoretic Bounded Rationality

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If you have a question about this talk, please contact Zoubin Ghahramani.

In this talk I provide an overview of information-theoretic bounded rationality for planning in sequential decision problems. I show how to ground the theory on a stochastic computation model for large-scale choice spaces and then derive the free energy functional as the associated variational principle for characterising bounded-rational decisions. These decision processes have three important properties: they trade off utility and decision complexity; they give rise to an equivalence class of behaviourally indistinguishable decision problems; and they possess natural stochastic choice algorithms. I will discuss a general class of bounded-rational sequential planning problems that encompasses some well-known classical planning algorithms as limit cases (such as Expectimax and Minimax), as well as trust- and risk-sensitive planning. Finally, I will point out formal connections to Bayesian inference and to regret theory.

Credits: This is joint work with Daniel A. Braun, Kee-Eung Kim, Daniel D. Lee, and Naftali Tishby (in alphabetical order).

This talk is part of the Machine Learning @ CUED series.

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