Assessing elaborated hypotheses: an interpretive case-based reasoning approach

  • Authors:

  • J. William Murdock;David W. Aha;Leonard A. Breslow

  • Affiliations:

  • Navy Center for Applied Research in Artificial Intelligence, Naval Research Laboratory, Washington, DC;Navy Center for Applied Research in Artificial Intelligence, Naval Research Laboratory, Washington, DC;Navy Center for Applied Research in Artificial Intelligence, Naval Research Laboratory, Washington, DC

  • Venue:
  • ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
  • Year:
  • 2003

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Abstract

Identifying potential terrorist threats is a crucial task, especially in our post 9/11 world. This task is performed by intelligence analysts, who search for threats in the context of an overwhelming amount of data. We describe AHEAD (Analogical Hypothesis Elaborator for Activity Detection), a knowledge-rich post-processor that analyzes automatically-generated hypotheses using an interpretive case-based reasoning methodology to help analysts understand and evaluate the hypotheses. AHEAD first attempts to retrieve a functional model of a process, represented in the Task-Method-Knowledge framework (Stroulia & Goel, 1995; Murdock & Goel, 2001), to identify the context of a given hypothesized activity. If retrieval succeeds, AHEAD then determines how the hypothesis instantiates the process. Finally, AHEAD generates arguments that explain how the evidence justifies and/or contradicts the hypothesis according to this instantiated process. Currently, we have implemented AHEAD's case (i.e., model) retrieval step and its user interface for displaying and browsing arguments in a human-readable form. In this paper, we describe AHEAD and detail its first evaluation. We report positive results including improvements in speed, accuracy, and confidence for users analyzing hypotheses about detected threats.