Hypothesis Management in Situation-Specific Network Construction

TL;DR

This paper introduces a framework for managing hypotheses in situation-specific network construction, specifically using Multi-entity Bayesian networks to handle uncertainty in complex domains like military vehicle tracking.

Contribution

It presents a novel approach to hypothesis management in MEBNs, balancing model accuracy and computational tractability in uncertain, dynamic environments.

Findings

Effective in inferring military group organization from reports

Balances model complexity with computational efficiency

Compared favorably to existing tracking and fusion methods

Abstract

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.