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Proceedings Paper

Adversarial intent modeling using embedded simulation and temporal Bayesian knowledge bases
Author(s): Nicholas J. Pioch; James Melhuish; Andy Seidel; Eugene Santos; Deqing Li; Mark Gorniak
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Paper Abstract

To foster shared battlespace awareness among air strategy planners, BAE Systems has developed Commander's Model Integration and Simulation Toolkit (CMIST), an Integrated Development Environment for authoring, integration, validation, and debugging of models relating multiple domains, including political, military, social, economic and information. CMIST provides a unified graphical user interface for such systems of systems modeling, spanning several disparate modeling paradigms. Here, we briefly review the CMIST architecture and then compare modeling results using two approaches to intent modeling. The first uses reactive agents with simplified behavior models that apply rule-based triggers to initiate actions based solely on observations of the external world at the current time in the simulation. The second method models proactive agents running an embedded CMIST simulation representing their projection of how events may unfold in the future in order to take early preventative action. Finally, we discuss a recent extension to CMIST that incorporates Temporal Bayesian Knowledge Bases for more sophisticated models of adversarial intent that are capable of inferring goals and future actions given evidence of current actions at particular times.

Paper Details

Date Published: 30 April 2009
PDF: 12 pages
Proc. SPIE 7348, Modeling and Simulation for Military Operations IV, 73480E (30 April 2009); doi: 10.1117/12.818926
Show Author Affiliations
Nicholas J. Pioch, BAE Systems (United States)
James Melhuish, BAE Systems (United States)
Andy Seidel, BAE Systems (United States)
Eugene Santos, Dartmouth College (United States)
Deqing Li, Dartmouth College (United States)
Mark Gorniak, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 7348:
Modeling and Simulation for Military Operations IV
Dawn A. Trevisani, Editor(s)

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