This article presents the results of a multifaceted research and development effort that synergistically integrates AI 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 the strategic center of gravity analysis, and the deployment and evaluation of these agents in several courses at the U.S. Army War College.
This article presents the results of a multifaceted research and development effort that synergistically integrates AI research with military strategy research and practical deployment of agents into education. A distinguishing feature of this collaboration is the synergistic integration of AI research with military strategy research and the practical use of agents in education, as detailed in the following. View on the Evolution of the Software Development Process. strategic leaders at all the United States senior military service colleges, there is a great emphasis on the center of gravity analysis (Strange 1996). Hence, we have the third objective of this research, the educational objective of enhancing the educational process of senior military officers through the use of intelligent agent technology. Both programs emphasized the use of innovative challenge problems to focus and evaluate the research and development efforts.
This paper presents the experience of a university research group that has successfully deployed an application of its artificial intelligence research. It identifies some of the factors that have contributed to this success, and proposes a framework for future deployment activities that are consistent with the mission of a research university.
For several years we have been developing the Disciple apprenticeship, multistrategy learning approach for building intelligent agents (Tecuci, 1998). The defining feature of the Disciple approach to building agents is that a person teaches the agent how to perform domain-specific tasks. This teaching of the agent is done in much the same way as teaching a student or apprentice, by giving the agent examples and explanations, as well as supervising and correcting its behavior. We claim that the Disciple approach significantly reduces the involvement of the knowledge engineer in the process of building an intelligent agent, most of the work being done directly by the domain expert. In this respect, the work on Disciple is part of a long term vision where personal computer users will no longer be simply consumers of ready-made software, as they are today, but also developers of their own software assistants.
This paper presents several design principles used in the development of the Disciple learning agents. The process of developing such an agent relies on importing ontologies from existing knowledge repositories, and on teaching the agent how to perform various tasks, in a way that resembles how an expert would teach a human apprentice when solving problems in cooperation. Experimental results support the usefulness of the presented principles which may be useful for the development of other agents.