How Learning Enables Intelligence Analysts to Rapidly Develop Practical Cognitive Assistants

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.