Representing and Learning Quality-Improving Search Control Knowledge

11 years 3 months ago
Representing and Learning Quality-Improving Search Control Knowledge
Generating good, production-quality plans is an essential element in transforming planners from research tools into real-world applications, but one that has been frequently overlooked in research on machine learning for planning. This paper describes quality, an architecture that automatically acquires operational quality-improving control knowledge given a domain theory, a domainspeci c metric of plan quality, and problems which provide planning experience. The framework includes two distinct domainindependent learning mechanisms which differ in the language used to represent the learned knowledge, namely control rules and control knowledge trees, and in the kinds of quality metrics for which they are best suited. quality is fully implemented on top of the prodigy4.0 nonlinear planner and its empirical evaluation has shown that the learned knowledge is able to substantially improve plan quality. Although the learning mechanisms have been developed for prodigy4.0, the framework is ge...
M. Alicia Pérez
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 1996
Where ICML
Authors M. Alicia Pérez
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