Uncertainty in Artificial Intelligence
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SPOOK: A System for Probabilistic Object-Oriented Knowledge Representation
Avi Pfeffer, Daphne Koller, Brian Milch, Ken Takusagawa
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.
Pages: 541-550
PS Link:
PDF Link: /papers/99/p541-pfeffer.pdf
AUTHOR = "Avi Pfeffer and Daphne Koller and Brian Milch and Ken Takusagawa",
TITLE = "SPOOK: A System for Probabilistic Object-Oriented Knowledge Representation",
BOOKTITLE = "Proceedings of the Fifteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-99)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1999",
PAGES = "541--550"

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