Embedding Learning in a General Frame-Based Architecture

This research seeks to .incorporate machine learning capabilities within a general-purpose frame-based architecture. We describe CHUNKER, an explanation-based chunking mechanism built on top of THEO, a software framework to support development of self-modifying problem solving systems. CHUNKER forms rules that improve problem solving efficiency, by generalizing and compressing the chains of inference which THEO produces during problem solving. After presenting the learning algorithm used by CHUNKER, we illustrate its application to learning search control knowledge, discuss its relationship to THEO’s other three learning mechanisms, and consider the relationship between architectural features of THEO and the effectiveness of CHUNKER.