KL control theory and decision making under uncertainty
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If you have a question about this talk, please contact Zoubin Ghahramani.
KL control theory consists of a class of control problems for which
the control computation can be solved as a graphical model inference
problem. In this talk, we show how to apply this theory in the context of
a delayed choice task and for collaborating agents. We first introduce
the KL control framework. Then we show that in a delayed reward task
when the future is uncertain it is optimal to delay the timing of your
decision. We show preliminary results on human subjects that confirm
this prediction. Subsequently, we discuss two player games, such as the
stag-hunt game, where collaboration can improve or worsten as a result
of recursive reasoning about the opponents actions. The Nash equilibria
appear as local minima of the optimal cost to go, but may disappear when
monetary gain decreases. This behaviour is in agreement with
experimental findings in humans.
This talk is part of the Machine Learning @ CUED series.
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Tuesday 10 November 2009, 11:00-12:00