Knowledge acquisition assisted by CLIPS programming

Abstract

Knowledge acquisition is important and machine learning techniques can be used to achieve automated knowledge acquisition. This article examines how knowledge acquisition can be assisted by programming using CLIPS (an acronym for C Language Integrated Production System). A machine learning preprocessor has been developed for the CLIPS environment, so that the CLIPS rule-base can be expanded by adding rules generated through machine learning techniques. The paper also shows how knowledge updating can be supported in the CLIPS environment itself. Operational engineering knowledge is captured in a data structure called a decision tree, and its structure can be updated when new knowledge is acquired. In addition, some advanced features are also briefly discussed, including using COOL (the CLIPS object-oriented language) for knowledge acquisition in a software product recommendation system, as well as the design of a self-evolving knowledge-acquisition tool.