Adaptive Information Fusion in Asymmetric Sensemaking Environment

Abstract

The existing sensemaking models for traditional force-on-force battlefield information management rarely survive the kinds of information in asymmetric battlespace environments. By combining the abduction process and Bayesian probability network formalisms, we propose a Bayesian Abduction Models (BAM) to support in the sensemaking process of evaluating multiple hypotheses in the context of changing information. This paper describes a Bayesian network that captures abduction logic primitives from a kernel of disparate information sources. We use a genetic learning algorithm to solve BAM information fusion problems. We show how the model can be used in prospective and retrospective sensemaking conditions to simulate the ways commanders and the battle staffs process information.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2007
Accession Number
ADA481629

Entities

People

  • Celestine A. Ntuen
  • Paul Munya

Organizations

  • North Carolina Agricultural and Technical State University

Tags

Communities of Interest

  • C4I
  • Weapons Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Bayes Theorem
  • Bayesian Networks
  • Command And Control
  • Computational Complexity
  • Computational Science
  • Environment
  • Genetic Algorithms
  • Hypotheses
  • Information Operations
  • Models
  • Neural Networks
  • Probabilistic Models
  • Probability
  • Reasoning
  • Simulations

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Systems Analysis and Design
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy
  • Biotechnology