Opportunistic Adaptation Knowledge Discovery

TL;DR

This paper proposes a method to automatically discover and opportunistically acquire adaptation knowledge during problem-solving, reducing the manual effort needed in case-based reasoning systems.

Contribution

It introduces a combined approach that learns adaptation knowledge from case bases and triggers acquisition opportunistically during problem-solving.

Findings

Effective knowledge discovery from case bases

Reduced manual effort in adaptation knowledge acquisition

Improved efficiency of case-based reasoning systems

Abstract

Adaptation has long been considered as the Achilles' heel of case-based reasoning since it requires some domain-specific knowledge that is difficult to acquire. In this paper, two strategies are combined in order to reduce the knowledge engineering cost induced by the adaptation knowledge (CA) acquisition task: CA is learned from the case base by the means of knowledge discovery techniques, and the CA acquisition sessions are opportunistically triggered, i.e., at problem-solving time.