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There is No Free Lunch: Tradeoffs in the Utility of Learned KnowledgeWith the recent introduction of learning in integrated systems, there is a need to measure the utility of learned knowledge for these more complex systems. A difficulty arrises when there are multiple, possibly conflicting, utility metrics to be measured. In this paper, we present schemes which trade off conflicting utility metrics in order to achieve some global performance objectives. In particular, we present a case study of a multi-strategy machine learning system, mutual theory refinement, which refines world models for an integrated reactive system, the Entropy Reduction Engine. We provide experimental results on the utility of learned knowledge in two conflicting metrics - improved accuracy and degraded efficiency. We then demonstrate two ways to trade off these metrics. In each, some learned knowledge is either approximated or dynamically 'forgotten' so as to improve efficiency while degrading accuracy only slightly.
Document ID
19960022269
Acquisition Source
Ames Research Center
Document Type
Technical Memorandum (TM)
Authors
Kedar, Smadar T. (NASA Ames Research Center Moffett Field, CA United States)
McKusick, Kathleen B. (NASA Ames Research Center Moffett Field, CA United States)
Date Acquired
September 6, 2013
Publication Date
June 12, 1992
Subject Category
Behavioral Sciences
Report/Patent Number
FIA-92-04
NAS 1.15:111483
NASA-TM-111483
Accession Number
96N25296
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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