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Abstract: General-purpose generative planners use domain-independent search heuristics to generate solutions for problems in a variety of domains. However, in some situations these heuristics force the planner to perform inefficiently or obtain solutions of poor quality. Learning from experience can help to identify the particular situations for which the domain-independent heuristics need to be overridden. Most of the past learning approaches are fully deductive and eagerly acquire correct control... (Update)
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BibTeX entry: (Update)
Daniel Borrajo and Manuela Veloso. Lazy incremental learning of control knowledge for efficiently obtaining quality plans. AI Review Journal. Special Issue on Lazy Learning, 10:1--34, 1996. http://citeseer.ist.psu.edu/article/borrajo96lazy.html More
@article{ borrajo97lazy,
author = "Daniel Borrajo and Manuela Veloso",
title = "Lazy Incremental Learning of Control Knowledge for Efficiently Obtaining Quality Plans",
journal = "AI Review Journal. Special Issue on Lazy Learning",
volume = "11",
number = "1-5",
pages = "371--405",
year = "1997",
url = "citeseer.ist.psu.edu/article/borrajo96lazy.html" }
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