This grant addresses a problem central to building intelligent systems, namely how to extract knowledge from data. This problem, known as "inductive concept learning," has received much attention in the machine learning community, and provides an alternative approach to the labor intensive and time consuming knowledge acquisition bottleneck by foregoing interaction with an expert altogether, and instead acquiring knowledge from case libraries. Although version spaces are one of the best known conceptual tools for concept learning, they suffer from three limitations that restrict their use as a practical tool for learning: computational intractability, noise intolerance, and representational inadequacy. This work proposes a three layered approach to overcome these limitations so that version spaces can be applied to practical problems while maintaining the attractive properties that make them a useful conceptual and analytical tool for concept learning.
