AIC Seminar Series
Learning Hierarchical Task Networks from Problem Solving
Notice: hosted by Karen Myers
Date: 2005-12-01 at 16:00
Location: EJ228 (Directions)
In this talk, I present a novel approach to representing, utilizing, and
learning hierarchical structures. The new formalism -- teleoreactive logic
programs -- is a special form of hierarchical task network that indexes
methods by the goals they achieve. These structures can be used for
reactive but goal-directed execution, and they can be interleaved with
problem solving over primitive operators to address tasks for which
there are no stored methods. Successful problem solving leads to the
incremental creation of new methods that handle analogous tasks directly
in the future. The learning module determines the structure of the
hierarchy, the heads or indices of component methods, and the conditions
on these methods. I report experiments on three domains that demonstrate
rapid learning of both disjunctive and recursive structures that transfer
well to more complex tasks. In closing, I discuss related research on
learning from problem solving and propose directions for future research.
This talk describes work done jointly with Dongkyu Choi and Seth Rogers.
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