Learning Hierarchical Task Networks
In this talk, I present a novel approach to representing, utilizing, and learning hierarchical structures. The new formalism - teleoreactive logic programs - involves 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.
The paper at http://cll.stanford.edu/~langley/papers/icarus.jmlr06.pdf reports this research in more detail. This talk describes work done jointly with Dongkyu Choi and Seth Rogers.