2013/01/10 by Kathryn Blackmond Laskey, Laskey, Kathryn Blackmond, Suzanne M. Mahoney +3
Computer Science · Decision Sciences · #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #Data Quality and Management #FOS: Computer and information sciences #Target Tracking and Data Fusion in Sensor Networks #cs.AI
paper · pdf · doi:10.48550/arxiv.1301.2287
Appears in Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI2001)
arxiv created 2013/01/10 · openalex publication_date 2013/01/10 · arxiv updated 2013/01/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper considers the problem of knowledge-based model construction in the presence of uncertainty about the association of domain entities to random variables. Multi-entity Bayesian networks (MEBNs) are defined as a representation for knowledge in domains characterized by uncertainty in the number of relevant entities, their interrelationships, and their association with observables. An MEBN implicitly specifies a probability distribution in terms of a hierarchically structured collection of Bayesian network fragments that together encode a joint probability distribution over arbitrarily many interrelated hypotheses. Although a finite query-complete model can always be constructed, association uncertainty typically makes exact model construction and evaluation intractable. The objective of hypothesis management is to balance tractability against accuracy. We describe an application to the problem of using intelligence reports to infer the organization and activities of groups of military vehicles. Our approach is compared to related work in the tracking and fusion literature.