Integrating Knowledge-Based and Case-Based Reasoning

Abstract

There has been substantial recent interest in integrating knowledge based reasoning (KBR) and case-based reasoning (CBR) within a single system due to the potential synergisms that could result. Here we describe our recent work investigating the feasibility of a combined KBR-CBR application-independent system for interpreting multi-episode stories/narratives, illustrating it with an application in the domain of interpreting urban warfare stories. A genetic algorithm is used to derive weights for selection of the most relevant past cases. In this setting, we examine the relative value of using input features of a problem for case selection versus using features inferred via KBR, versus both. We find that using both types of features is best (compared to human selection), but that input features are most helpful and inferred features are of marginal value. This finding supports the idea that KBR and CBR provide complimentary rather than redundant information, and hence that their combination in a single system is likely to be useful.