Predictive situation awareness reference model using Multi-Entity Bayesian Networks

Predictive Situation Awareness (PSAW) emphasizes the ability to make predictions about aspects of a temporally evolving situation. Higher-level fusion to support PSAW requires a semantically rich representation to handle complex real world situations and the ability to reason under uncertainty about...

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Bibliographic Details
Main Authors: Cheol Young Park, Laskey, Kathryn Blackmond, Costa, Paulo C. G., Matsumoto, Shou
Format: Conference Proceeding
Language:English
Subjects:
Bayes methods
Context
Data Fusion
Defense System
Multi-Entity Bayesian Networks
Object recognition
Predictive Situation Awareness
Random variables
Situation Awareness
Surface acoustic waves
Uncertainty
Vehicles
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Summary:Predictive Situation Awareness (PSAW) emphasizes the ability to make predictions about aspects of a temporally evolving situation. Higher-level fusion to support PSAW requires a semantically rich representation to handle complex real world situations and the ability to reason under uncertainty about the situation. Multi-Entity Bayesian Networks (MEBN) are rich enough to represent and reason about uncertainty in complex, knowledge-rich domains. In previous applications of MEBN to PSAW, the models, called MTheories, were constructed from scratch for each application. Designing models from scratch is inefficient and fails to build on the experience gained from prior work. In this paper, we argue that applications of MEBN to PSAW share similar goals and common model elements. We propose a reference model for designing a MEBN model for PSAW and evaluate our model on a case study of a defense system.