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AAAI
1998

Learning to Predict User Operations for Adaptive Scheduling

13 years 5 months ago
Learning to Predict User Operations for Adaptive Scheduling
Mixed-initiativesystemspresent the challengeof finding an effective level of interaction betweenhumans and computers. Machinelearning presents a promising approachto this problemin the form of systems that automatically adapt their behavior to accommodate different users. In this paper, wepresent an empirical study of learning user modelsin an adaptive assistant for crisis scheduling.Wedescribe the problemdomainand the schedulingassistant, then present an initial formulationof the adaptiveassistant's learning task andthe results of a baselinestudy. Afterthis, wereport the results of three subsequentexperiments that investigate the effects of problemreformulation and representation augmentation.Theresults suggest that problemreformulationleads to significantly better accuracywithoutsacrificing the usefulnessof the learned behavior.Thestudies also raise several interesting issues in adaptiveassistancefor scheduling.
Melinda T. Gervasio, Wayne Iba, Pat Langley
Added 01 Nov 2010
Updated 01 Nov 2010
Type Conference
Year 1998
Where AAAI
Authors Melinda T. Gervasio, Wayne Iba, Pat Langley
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