A process for human-aided Multi-Entity Bayesian Networks learning in Predictive Situation Awareness
Cheol Young Park, Kathryn Blackmond Laskey, Paulo Costa, Shou Matsumoto · International Conference on Information Fusion · 2016
Predictive Situation Awareness (PSAW) is the ability to estimate and predict aspects of a temporally evolving situation. PSAW systems reason about complex and uncertain situations involving multiple targets observed by multiple sensors at different times. Multi-Entity Bayesian Networks (MEBN) are rich enough to represent and reason about uncertainty in complex, knowledge-rich domains, and have been applied to representation and reasoning for PSAW. To overcome a labor-intensive and insufficiently agile process for manual MEBN modeling by a domain expert, MEBN machine learning was developed. Although technologies for machine learning have improved dramatically, the necessary capabilities to build a MEBN model efficiently do not yet exist. The search space for components of an MEBN model is too large and complex to investigate all possible structures, variables, and parameters. For this reason, this paper proposes a method which relies partially on expert knowledge and insight to reduce the search space. The proposed method, a process for Human-aided MEBN learning in PSAW, is a framework to develop a MEBN model from the domain expert's knowledge combined with relational data. This paper presents the process for Human-aided MEBN learning in PSAW and a case study to evaluate the process on development of a defense system in PSAW.