Learning tasks from a single demonstration presents a significant challenge because the observed sequence is specific to the current situation and is inherently an incomplete representation of the procedure. Observation-based machine-learning techniques are not effective without multiple examples. However, when a demonstration is accompanied by natural language explanation, the language provides a rich source of information about the relationships between the steps in the procedure and the decision-making processes that led to them. In this article, we present a one-shot task learning system built on TRIPS, a dialogue-based collaborative problem solving system, and show how natural language understanding can be used for effective one-shot task learning.
['Hyuckchul Jung', 'James F. Allen', 'Lucian Galescu', 'Nathanael Chambers', 'Mary D. Swift', 'William Taysom']
Utilizing Natural Language for One-Shot Task Learning