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During the past quarter-century, situation awareness (SAW) has become acritical research theme, because of its importance. Since the concept of SAWwas first introduced during World War I, various versions of SAW have beenresearched and introduced. Predictive Situation Awareness (PSAW) focuses on theability to predict aspects of a temporally evolving situation over time. PSAWrequires a formal representation and a reasoning method using such arepresentation. A Multi-Entity Bayesian Network (MEBN) is a knowledgerepresentation formalism combining Bayesian Networks (BN) with First-OrderLogic (FOL). MEBN can be used to represent uncertain situations (supported byBN) as well as complex situations (supported by FOL). Also, efficient reasoningalgorithms for MEBN have been developed. MEBN can be a formal representation tosupport PSAW and has been used for several PSAW systems. Although several MEBNapplications for PSAW exist, very little work can be found in the literaturethat attempts to generalize a MEBN model to support PSAW. In this research, wedefine 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 forPSAW by supporting the design of a MEBN model for PSAW. In this research, weintroduce two example use cases using the PSAW-MEBN reference model to developMEBN models to support PSAW: a Smart Manufacturing System and a Maritime DomainAwareness System.

Reference Model of Multi-Entity Bayesian Networks for Predictive Situation Awareness