Remembrance of Things Past
Leslie Pack Kaelbling
Brown University
Abstract
Behaving effectively in partially observable environments requires
remembering something about the past. Partially observable Markov
decision processes (POMDPs) provide a formal model for describing and
evaluating control problems that require memory. An agent in an
unknown POMDP has two main strategies: learn a model of the POMDP,
then solve for a good policy or learn a policy directly.
I will begin by describing an algorithm for learning POMDP models
for robot navigation, which, coupled with previous work on controlling
POMDPs, yields a behavior learning system. Then, I'll talk about some
very recent work on direct approaches to learning policies for POMDPs
without first learning a model. I'll conclude with a description of a
new project on learning models for visual navigation in humans and
robots.
Biographical Information
Leslie Pack Kaelbling is Associate Professor of Computer Science at
Brown University. She previously held positions at the Artificial
Intelligence Center of SRI International and at Teleos Research. She
received an A. B. in Philosophy in 1983 and a Ph. D. in Computer
Science in 1990, both from Stanford University. Prof. Kaelbling has
done substantial research on programming paradigms and languages for
embedded systems, on mobile robot design and implementation, and on
reinforcement learning algorithms. Her current research directions
include integrating learning modules into systems programmed by
humans, algorithms for learning and navigating using hierarchical
domain representations, and methods for learning perceptual
strategies. She is an NSF Presidential Faculty Fellow, a member of
the AAAI Executive Council, a member of the IJCAI Advisory Committee,
and the 1997 recipient of the IJCAI Computers and Thought Award.
Eyal Amir
Last modified: Wed Mar 3 16:39:42 PST 1999