Learning complex problem solving expertise from failures

Our research addresses the issue of developing knowledge-based agents that capture and use the problem solving knowledge of subject matter experts from diverse application domains. This paper emphasizes the use of negative examples in agent learning by presenting several strategies for capturing expert's knowledge when the agent fails to correctly solve a problem. These strategies have been implemented into the disciple learning agent shell and used in complex application domains such as intelligence analysis, center of gravity determination, and emergency response planning.