In this position paper, we discuss the interdependencies of context models and flexible design processes in the chip industry. We illustrate this by an ontology-based context model...
Abstract. Creating case representations in unsupervised textual case-based reasoning applications is a challenging task because class knowledge is not available to aid selection of...
Stewart Massie, Nirmalie Wiratunga, Susan Craw, Al...
A knowledge-intensive case-based reasoning system has profit of the domain knowledge, together with the case base. Therefore, acquiring new pieces of domain knowledge should impro...
Abstract. Making case adaptation practical is a longstanding challenge for casebased reasoning. One of the impediments to widespread use of automated case adaptation is the adaptat...
Case-based reasoning systems routinely record the results of prior problem-solving, but not the provenance of new cases: the way in which the new cases were derived. This paper pro...
Current efficient planners employ an informed search guided by a heuristic function that is quite expensive to compute. Thus, ordering nodes in the search tree becomes a key issue,...
In this paper, we compare case-based spam filters, focusing on their resilience to concept drift. In particular, we evaluate how to track concept drift using a case-based spam fi...
Case-Based Reasoning (CBR) is a methodology that reuses the solutions of previous similar problems to solve new problems. Adaptation is the most difficult stage in the CBR cycle, e...
We identify two fundamental points of utilizing CBR for an adaptive agent that tries to learn on the basis of trial and error without a model of its environment. The first link co...