Agent-based knowledge evolution management and fuzzy rule-based evolution detection in Bayesian networks

All data and information are not always available at the time of a system design and implementation. Especially in knowledge-based systems, training data could be limited at the early stage and more training data might be acquired after the system deployment. This paper is concerned with a method to keep track of knowledge evolution and to detect the changes in the knowledge as more training data are provided. The method assumes that the knowledge is expressed in Bayesian networks and makes use of an agent framework for autonomous processing of knowledge evolution and change detection. It maintains sufficient statistics using a tiled sliding window structure. In order to flexibly encode the strategy for detecting the changes in the joint probability distributions, a set of fuzzy rules are used with which application domains specify their own strategy.