NAC: An Adaptive Case-Based Reasoning Tool for Experimenting with Retrieval and Indexing

Abstract

Abstract : NAC is a testbed for experimenting with concepts of retrieval and indexing in Case-Based Reasoning (CBR). The paper describes similarity functions and decision functions used for retrieval as well as methods for re-indexing and case organization. The paper also describes methods for weighting attributes, analyzing their dependence and evaluating the importance index of a single stored case. Two examples of how to use NAC are provided. Methods employed for retrieval and indexing are based on mathematically sound techniques developed in classification, clustering and decision analysis. NAC includes basic functions for specifying similarity, normalizing data and evaluation. Retrieval is done using both nonparametric (e.g., nearest neighbor) and parametric (i.e., Bayesian) statistical procedures with weighted attributes. New indices for cases are generated using clustering methods. Cases are re-organizing using the new indices. NAC also allows the user to test the predictive accuracy of retrieval methods and the quality of indices generated in re-indexing. NAC includes adaptive functions for enhancing the performance of retrieval and indexing. Important functions for adaptation include weighting and selecting attributes, learning dependency relationships and calculating typicality for each stored case. The NAC environment was designed so that additional techniques and metrics can easily be added.