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- W2288052980 abstract "In this thesis we explore the idea of improving the control strategy of a problem solver by reducing the search-control errors that it commits. We define a search-control error as any decision to expand a path that eventually turns out to be inferior to another path that the search- control strategy could have selected for expansion instead. The number of search-control errors is not reduced by explicitly repairing the control strategy to prevent their occurrence. Instead, we define the notion of an imbalance in the path evaluations made by the control strategy. Given a graph produced by searching for a problem's solution, imbalances located in critical areas of the graph are called search-control mistakes. We show that correcting these search-control mistakes prevents the search-control errors made during the graph's creation and also precludes new errors that have not yet been made; these new errors would not be prevented had we fixed errors instead of mistakes. The search-control mistakes are corrected by learning informed composite productions that combine the characteristics of macros and meta-rules. An Explanation-Based Learning method for creating such productions is described and the performance improvements that result from the refined control strategy are demonstrated in a test domain of symbolic integration." @default.
- W2288052980 created "2016-06-24" @default.
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- W2288052980 date "1990-01-03" @default.
- W2288052980 modified "2023-09-26" @default.
- W2288052980 title "Failure-driven incremental search-control refinement" @default.
- W2288052980 hasPublicationYear "1990" @default.
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