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Journal of Logic and Computation Advance Access published online on December 20, 2007
Journal of Logic and Computation, doi:10.1093/logcom/exm071
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© The Author 2007. Published by Oxford University Press on behalf of the Association of Physicians. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Original papers

Utilizing Natural Language for One-Shot Task Learning

Hyuckchul Jung, James Allen and Lucian Galescu

Florida Institute for Human and Machine Cognition, Pensacola, FL 32502, USA. email: hjung{at}ihmc.us, jallen{at}ihmc.us, lgalescu{at}ihmc.us

Nathanael Chambers

Department of Computer Science, Stanford University, Stanford, CA 94305, USA email: natec{at}stanford.edu

Mary Swift

Computer Science Department, University of Rochester, Rochester, NY 14627, USA. email: swift{at}cs.rochester.edu

William Taysom

Florida Institute for Human and Machine Cognition, Pensacola, FL 32502, USA. email: wtaysom{at}ihmc.us

Received 31 July 2006.


  Abstract

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.


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