Reference Model of Multi-Entity Bayesian Networks for Predictive
  Situation Awareness
  During the past quarter-century, situation awareness (SAW) has become a
critical research theme, because of its importance. Since the concept of SAW
was first introduced during World War I, various versions of SAW have been
researched and introduced. Predictive Situation Awareness (PSAW) focuses on the
ability to predict aspects of a temporally evolving situation over time. PSAW
requires a formal representation and a reasoning method using such a
representation. A Multi-Entity Bayesian Network (MEBN) is a knowledge
representation formalism combining Bayesian Networks (BN) with First-Order
Logic (FOL). MEBN can be used to represent uncertain situations (supported by
BN) as well as complex situations (supported by FOL). Also, efficient reasoning
algorithms for MEBN have been developed. MEBN can be a formal representation to
support PSAW and has been used for several PSAW systems. Although several MEBN
applications for PSAW exist, very little work can be found in the literature
that attempts to generalize a MEBN model to support PSAW. In this research, we
define a reference model for MEBN in PSAW, called a PSAW-MEBN reference model.
The PSAW-MEBN reference model enables us to easily develop a MEBN model for
PSAW by supporting the design of a MEBN model for PSAW. In this research, we
introduce two example use cases using the PSAW-MEBN reference model to develop
MEBN models to support PSAW: a Smart Manufacturing System and a Maritime Domain
Awareness System.
