Sponsor: ESC Contract No.: FA9721-04-0001

This paper presents the results of a multi-faceted research and development effort that synergistically integrates artificial intelligence research with military strategy research and practical deployment of agents into education. It describes recent advances in the Disciple approach to agent development by subject matter experts with limited assistance from knowledge engineers, the innovative application of Disciple to the development of agents for strategic center of gravity analysis, and the deployment and evaluation of these agents in several courses at the US Army War College

This paper presents new significant advances in the Disciple approach for building knowledge-based systems by subject matter experts. It describes the innovative application of this approach to the development of an agent for the analysis of strategic centers of gravity in military conflicts. This application has been deployed in several courses at the US Army War College, and its use has been evaluated. The presented results are those of a multi-faceted research and development effort that synergistically integrates research in Artificial Intelligence, Center of Gravity analysis, and practical deployment of an agent into Education. 1

Over the years we have developed the Disciple theory, methodology, and family of tools for building knowledge-based agents. This approach consists in developing an agent shell that can be taught directly by a subject matter expert, in a way that resembles how the expert would teach a human apprentice when solving problems in cooperation. This paper presents the most recent version of the Disciple approach and its implementation in the Disciple-RKF system. Disciple-RKF is based on methods for mixed-initiative problem solving, where the expert solves the more creative problems and the agent solves the more routine ones, integrated teaching and learning, where the agent helps the expert to teach it, by asking relevant questions, and the expert helps the agent to learn, by providing examples, hints and explanations, and multistrategy learning, where the agent integrates multiple learning strategies, such as learning from examples, learning from explanations, and learning by analogy, to learn from the expert how to solves problems. Disciple-RKF has been successfully applied to build learning and reasoning agents for military center of gravity analysis, which are used in several courses at the US Army War College. Key Words: multistrategy apprenticeship learning, problem solving through task reduction, mixed-initiative reasoning, plausible version spaces, rule learning, ontology, agent development, military center of gravity analysis 2 1.

This paper addresses the problem of improving the representation space in a rule-based intelligent system, through exception-based learning. Such a system generally learns rules containing exceptions because its representation language is incomplete. However, these exceptions suggest what may be missing from the system's ontology, which is the basis of the representation language. We describe an interactive exception-based learning method for eliciting new elements in the system's ontology in order to eliminate the exceptions of the rules. This method is implemented in the Disciple learning agent shell and has been evaluated in an agent training experiment at the US Army War College. 1

Abstract- This paper presents the Disciple-RKF methodology for rapid development of large knowledge bases which relies on importing ontological knowledge from existing knowledge repositories, on parallel development of separate knowledge bases by subject matter experts, and on the merging of these knowledge bases into a high performance integrated knowledge base. The paper discusses several issues related to ontology import and merging, and presents the results of a successful knowledge base development and integration experiment performed at the US Army War College.

This chapter presents an overview of the Disciple approach and its military applications. Disciple is a theory, methodology, and family of agent shells for the development of intelligent agents by subject matter experts, with limited assistance from computer scientists or knowledge engineers [1]. A subject matter