Analogy, Knowledge Integration, and Task Modeling Tools for Intelligence Analysts

Sponsor: National Science Foundation, Knowledge Discovery and Dissemination Program

Principle Investigators: Kenneth D. Forbus, Lawrence Birnbaum, Douglas Lenat (Cycorp)

Project Summary: Intelligence analysts are the knowledge workers most directly involved in our nation's defense. Analysts must sift through massive amounts of data, using perspective gained from history and experience to pull together from disparate sources the best coherent picture of what is happening. Information Technology research has the potential to create new software tools that could aid analysts in several ways:

Current technology is capable of providing some of this functionality, but in a limited and piecemeal manner. Knowledge-based systems offer fine-grained and logically coherent inferences and hypotheses — deduction and induction — but only when a sufficiently large fraction of all relevant information is both present and represented precisely (e.g., in formal logic, if-then rules, etc.). Analogical reasoning systems offer the prospect of "thinking outside the box" — but again depend upon structured representations. IR (Information Retrieval) systems can handle the quantity and diversity of unstructured information that exists in the world, but cannot generate new inferences or hypotheses.

We are integrating and extending these three technologies to create power tools for intelligence analysts. We expect this to have two simultaneous effects: to advance the state of research in each of our areas, and to lay the groundwork (including prototypes) for developing the broad yet smart software assistants intelligence analysts need.

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