Reference Model of Multi-Entity Bayesian Networks for Predictive Situation Awareness
Cheol Young Park, Kathryn Blackmond Laskey

TL;DR
This paper introduces a standardized reference model for Multi-Entity Bayesian Networks to enhance predictive situation awareness, facilitating easier development of PSAW systems across various domains.
Contribution
It defines a general PSAW-MEBN reference model that supports the design and implementation of MEBN-based PSAW systems, filling a gap in existing literature.
Findings
Developed a PSAW-MEBN reference model for easier system design.
Applied the model to smart manufacturing and maritime awareness systems.
Demonstrated the model's utility through example use cases.
Abstract
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…
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Taxonomy
TopicsBayesian Modeling and Causal Inference · AI-based Problem Solving and Planning · Semantic Web and Ontologies
