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

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

View Publication

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.


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 e ciency 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.

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},
year = {1995},
month = {January},
pages = {371 - 376},
} 2017-09-13T10:47:10-04:00