Interactive Learning of Hierarchical Tasks from Dialog with GPT
Lane Lawley Christopher J. MacLellan
We present a system for interpretable, symbolic, interactive task learning
from dialog using a GPT model as a conversational front-end. The learned tasks
are represented as hierarchical decompositions of predicate-argument structures
with scoped variable arguments. By using a GPT model to convert interactive
dialog into a semantic representation, and then recursively asking for
definitions of unknown steps, we show that hierarchical task knowledge can be
acquired and re-used in a natural and unrestrained conversational environment.
We compare our system to a similar architecture using a more conventional
parser and show that our system tolerates a much wider variety of linguistic
variance.