Cristina Boicu

Training and Using DISCIPLE Agents A Case Study in the Military Center of Gravity Analysis Domain

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.

THE DISCIPLE–RKF LEARNING AND REASONING AGENT

Over the years we have developed the Disciple theory, methodology, and family of tools for building knowledge‐based agents. This approach consists of 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 (rapid knowledge formation) system. Disciple–RKF is based on mixed‐initiative problem solving, where the expert solves the more creative parts of the problem 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 solve problems. Disciple–RKF has been 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.

Parallel Knowledge Base Development by Subject Matter Experts

This paper presents an experiment of parallel knowledge base development by subject matter experts, performed as part of the DARPA’s Rapid Knowledge Formation Program. It introduces the Disciple-RKF development environment used in this experiment and proposes design guidelines for systems that support authoring of problem solving knowledge by subject matter experts. Finally, it compares Disciple-RKF with the other development environments from the same DARPA program, providing further support for the proposed guidelines.

A Tool for Training and Assistance in Emergency Response Planning

This paper presents research on developing a new type of software tool for training and assisting the personnel in emergency response planning. The tool, called Disciple-VPT, would include a library of virtual planning experts, each with a certain level of expertise (such as basic, intermediate or advanced) in one of the 15 emergency support functions defined by the US national response plan. Disciple-VPT can be used in a variety of training exercises where responders learn from virtual experts how to collaborate in emergency response planning. The development of Disciple-VPT is facilitated by the Disciple-VE learning agent shell that can be taught by a subject matter expert how to plan, trough examples and explanations, in a way that is similar to how the expert would teach an apprentice

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

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 experts 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.