An Empirical Study of Knowledge Tradeoffs in Case-Based Reasoning
An Empirical Study of Knowledge Tradeoffs in Case-Based Reasoning
Devi Ganesan, Sutanu Chakraborti
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Main track. Pages 1817-1823.
https://doi.org/10.24963/ijcai.2018/251
Case-Based Reasoning provides a framework for integrating domain knowledge with data in the form of four knowledge containers namely Case base, Vocabulary, Similarity and Adaptation. It is a known fact in Case-Based Reasoning community that knowledge can be interchanged between the containers. However, the explicit interplay between them, and how this interchange is affected by the knowledge richness of the underlying domain is not yet fully understood. We attempt to bridge this gap by proposing footprint size reduction as a measure for quantifying knowledge tradeoffs between containers. The proposed measure is empirically evaluated on synthetic as well as real world datasets. From a practical standpoint, footprint size reduction provides a unified way of estimating the impact of a given piece of knowledge in any knowledge container, and can also suggest ways of characterizing the nature of domains ranging from ill-defined to well-defined ones. Our study also makes evident the need for maintenance approaches that go beyond case base and competence to include other containers and performance objectives.
Keywords:
Knowledge Representation and Reasoning: Case-based reasoning
Knowledge Representation and Reasoning: Knowledge Representation and Decision ; Utility Theory