This paper overviews an end-to-end learning-based approach to the rapid development of practical cognitive assistants for intelligence analysis. A learning agent shell has been trained by a knowledge engineer with general evidence-based reasoning knowledge for intelligence analysis. This agent is further trained by an expert analyst how to analyze complex hypotheses from a given intelligence analysis domain. The resulting cognitive assistant is used by a typical analyst to rapidly analyze hypotheses from agent's area of expertise. During its use, the agent continues to learn reasoning patterns from its user. This approach has been implemented and practical agents have been developed and used. This is a significant application of machine learning to agents development in intelligence analysis that can be generalized to many other domains involving evidence-based reasoning, including medicine, law, and science.

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.

This paper presents current results in developing a practical approach, methodology and tool, for the development of knowledge bases and agents by subject matter experts, with limited assistance from knowledge engineers. This approach is based on mixed-initiative reasoning that integrates the complementary knowledge and reasoning styles of a subject matter expert and a learning agent, and on a division of responsibilities for those elements of knowledge engineering for which they have the most aptitude. The approach was evaluated at the US Army War College, demonstrating very good results and a high potential for overcoming the knowledge acquisition bottleneck.