Seminar on Computational Learning and Adaptation

   Learning Hierarchical Task Networks
     from Problem Solving

Pat Langley
Computational Learning Laboratory
Center for the Study of Language and Information
Stanford University
http://cll.stanford.edu/~langley/
 

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 which 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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