Learning to predict user operations for adaptive scheduling

Authors:
Melinda T. Gervasio;Wayne Iba;Pat Langley
Affiliations:
-;-;-
Venue:
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Year:
1998

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Abstract

Mixed-initiative systems present the challenge of finding an effective level of interaction between humans and computers. Machine learning presents a promising approach to this problem in the form of systems that automatically adapt their behavior to accommodate different users. In this paper, we present an empirical study of learning user models in an adaptive assistant for crisis scheduling. We describe the problem domain and the scheduling assistant, then present an initial formulation of the adaptive assistant's learning task and the results of a baseline study. After this, we report the results of three subsequent experiments that investigate the effects of problem reformulation and representation augmentation. The results suggest that problem reformulation leads to significantly better accuracy without sacrificing the usefulness of the learned behavior. The studies also raise several interesting issues in adaptive assistance for scheduling.