Forgetting Cases by Restricting Memory

Abstract

One of the important issues of case-based reasoning is its adaptability for the change of environmental conditions. This means that cases which were useful for solving problems in the past often become unsuitable since environmental conditions would change as time passes. To solve such a problem, we have proposed a method of case management based on 'forgetting', which is supposed to imitate the human memory for removing obsolete cases. Though the method is very effective in adapting the case base to the environment, imitating the human memory may not always be the best method. For instance, while the system we implemented extends the term of remembrance of cases which are recalled during solving problems, a strategy which removes those cases is also plausible because a solution of a current problem is saved as a new case to the case base and it can cover for the old case which is utilized in solving the problem. In this paper, we propose a strategy for forgetting cases for a memory-restricted forgetting mechanism which is implemented by defining the size of the case base, i. e. the maximum number of cases is N, and replacing an old case with a new case when the number of cases reached N. We describe three basic strategies for the forgetting mechanism and propose extended forgetting strategies, i. e. ・ combination of the basic strategies and ・ the strategy which uses heuristics. The effectivness of the proposed strategies for improving the performance of case-based reasoning systems is demonstrated through simulations in the electric power systems.