Lazy rule refinement by knowledge-based agents

This paper presents recent results on developing learning agents that can be taught by subject matter experts how to solve problems, through examples and explanations. It introduces the lazy rule refinement method where the expert modifies an example generated by a learned rule. In this case the agent has to decide whether to modify the rule (if the modification applies to all the previous positive examples) or to learn a new rule. However, checking the previous examples would be disruptive or even impossible. The lazy rule refinement method provides an elegant solution to this problem, in which the agent delays the decision whether to modify the rule or to learn a new rule until it accumulated enough examples during the follow-on problem solving process. This method has been incorporated into the Disciple learning agent shell and used in the complex application areas of center of gravity analysis and intelligence analysis.