Applying Spatial-Temporal Model and Game Theory to Asymmetric Threat Prediction
Abstract
Accurate predictions of enemy course of actions "ECOA" are important to the command and control optimization strategies in long-lasting battles. In most Command and Control "C2" applications, the existing techniques, such as spatial-temporal point models for ECOA prediction or Discrete Choice Model "DCM", assume that insurgent attack features/patterns, or at least the trends of behavior patterns, are static. However, this static assumption is no longer true for intelligent and organized insurgents in recent antiterrorism war. These insurgents sometimes deliberately violate probability theory predictions so they can apply surprise attacks to create more casualties and spread terror. In this paper, a new game theoretic framework is proposed for modeling dynamic changes of enemy behavior features and predicting future threats. This framework semantically combines several different approaches; namely, a feature prediction game, higher level hybrid data fusion, techniques to provide concrete spatial-temporal modeling and prediction, emotion analysis of adversary rationality and non-rationality, deception identification and modeling, hierarchical knowledge representation, and a non-zero sum stochastic adversarial Markov game. We mainly describe the modification of existing spatial-temporal point models, the fusion of dynamic game feature selection technique and dynamic cohesiveness feature selection technique, the ontology about selected/unselected features, and construction of probability predictions.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 01, 2007
- Accession Number
- ADA481228
Entities
People
- Genshe Chen
- Jose B. Cruz Jr.
- Leonard Haynes
- Martin Krüger
- Mo Wei
Organizations
- Office of Naval Research