SPOOK: A System for Probabilistic Object-Oriented Knowledge Representation
This addresses the problem of representing complex, uncertain domains with named objects and arbitrary relations for applications such as battlefield awareness, offering a significant improvement over prior methods.
The paper tackles the limitations of Object-oriented Bayesian Networks (OOBNs) in modeling complex domains like battlefield awareness by introducing SPOOK, a system with a more expressive language and a new inference algorithm, achieving orders of magnitude speedup over existing approaches.
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.