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Abstract : |
Adding knowledge to a knowledge-based system is not monotonically beneficial. We discuss and experimentally validate this observation in the context of CABINS, a system that learns control knowledge for iterative repair in ill-structured optimization problems. In CABINS, situation-dependent user's decisions that guide the repair process are captured in cases together with contextual problem information. During iterative revision in CABINS, cases are exploited for both selection of repair actions and evaluation of repair results. In this paper, we experimentally demonstrated that unfiltered learned knowledge can degrade problem solving performance. We developed and experimentally evaluated the effectiveness of a set of knowledge filtering strategies that are designed to increase problem solving efficiency of the intractable iterative optimization process without sacrificing solution quality. These knowledge filtering strategies utilize progressive case base retrievals and failure information to (1) validate the effectiveness of selected repair actions and (2) give-up further repair if the likelihood of success is low. The filtering strategies were experimentally evaluated in the context of job-shop scheduling, a well known ill-structured problem., |
