Hypothesis Management in Situation-Specific Network Construction
  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.
