Overcoming Intelligence Analysis Complexity with Cognitive Assistants

This paper presents a computational approach to intelligence analysis which is viewed as ceaseless discovery in a non-stationary world involving concurrent processes of evidence in search of hypotheses, hypothesis in search of evidence, and evidential tests of hypotheses. This approach is at the basis of Disciple-LTA, a cognitive assistant that helps intelligence analysts evaluate the likelihood of hypotheses by developing Wigmorean probabilistic inference networks that link evidence to hypotheses in argumentation structures that establish the relevance, believability and inferential force or weight of evidence. The paper also shows how the intelligence analysis concepts and methods embedded into Disciple-LTA, which are based on the Science of Evidence and Artificial Intelligence, can be used to improve other structured analytic methods, using Analysis of Competing Hypothesis as an example.