Improving System Performance in Case-Based Iterative Optimization through Knowledge Filtering

K. Miyashita and Katia Sycara
Proc. of the International Joint Conference on Artificial Intelligence, 1995, pp. 371 - 376.


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Abstract
Adding knowledge to a knowledge-based system is not monotonically bene cial. 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 un ltered learned knowledge can degrade problem solving performance. We developed and experimentally evaluated the e ectiveness of a set of knowledge ltering strategies that are designed to increase problem solving eciency of the intractable iterative optimization process without sacri cing solution quality. These knowledge ltering strategies utilize progressive casebase retrievals and failure information to (1) validate the e ectiveness of selected repair actions and (2) give-up further repair if the likelihood of success is low. The ltering strategies were experimentally evaluated in the context of job-shop scheduling, a well known ill-structured problem.

Notes
Associated Center(s) / Consortia: Center for Integrated Manfacturing Decision Systems
Associated Lab(s) / Group(s): Case Based Reasoning Lab
Associated Project(s): CABINS

Text Reference
K. Miyashita and Katia Sycara, "Improving System Performance in Case-Based Iterative Optimization through Knowledge Filtering," Proc. of the International Joint Conference on Artificial Intelligence, 1995, pp. 371 - 376.

BibTeX Reference
@inproceedings{Sycara_1995_2634,
   author = "K. Miyashita and Katia Sycara",
   title = "Improving System Performance in Case-Based Iterative Optimization through Knowledge Filtering",
   booktitle = "Proc. of the International Joint Conference on Artificial Intelligence",
   pages = "371 - 376",
   year = "1995",
}