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home · events · colloquia · 1996-1997 · 

Colloquium - Sutton

ECCR 265

Knowledge Representation and Reinforcement Learning
University of Massachusetts, Amherst
Richard Sutton photo

Reinforcement learning has been a principled but impoverished approach to artificial intelligence -- principled because it is grounded in experience and in the mathematics of Markov decision processes (MDPs), but impoverished in that it is only able to use world knowledge when represented a constrained way, in particular, at a uniform temporal scale. The reinforcement learning agent that learns to throw a baseball cannot then learn where to throw it, or how to find its way to the playing field. The challenge is to find a knowledge representation language that is expressive and flexible, not unlike the rules of classical symbolic AI, and yet has a mathematically explicit semantics, like the state-transition probabilities of MDPs.

In this talk I propose "multi-time models," a mathematical framework for representing the dynamics of the world in a useful and temporally abstract way. The form of multi-time models is dictated, apparently uniquely, by the requirements for 1) temporal flexibility and expressiveness, 2) suitability for MDP-style planning, and 3) learnability. I present theoretical and conceptual results, illustrated by computational examples. This is joint work with Doina Precup.

Refreshments will be served immediately before the talk at 3:30pm.
Hosted by Satinder Singh.

The Department holds colloquia throughout the Fall and Spring semesters. These colloquia, open to the public, are typically held on Thursday afternoons, but sometimes occur at other times as well. If you would like to receive email notification of upcoming colloquia, subscribe to our Colloquia Mailing List. If you would like to schedule a colloquium, see Colloquium Scheduling.

Sign language interpreters are available upon request. Please contact Stephanie Morris at least five days prior to the colloquium.

See also:
Department of Computer Science
College of Engineering and Applied Science
University of Colorado Boulder
Boulder, CO 80309-0430 USA
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