SPOOK: A System for Probabilistic Object-Oriented Knowledge
  Representation
  In previous work, we pointed out the limitations of standard Bayesian
networks as a modeling framework for large, complex domains. We proposed a new,
richly structured modeling language, {em Object-oriented Bayesian Netorks},
that we argued would be able to deal with such domains. However, it turns out
that OOBNs are not expressive enough to model many interesting aspects of
complex domains: the existence of specific named objects, arbitrary relations
between objects, and uncertainty over domain structure. These aspects are
crucial in real-world domains such as battlefield awareness. In this paper, we
present SPOOK, an implemented system that addresses these limitations. SPOOK
implements a more expressive language that allows it to represent the
battlespace domain naturally and compactly. We present a new inference
algorithm that utilizes the model structure in a fundamental way, and show
empirically that it achieves orders of magnitude speedup over existing
approaches.
