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Daphne Koller Publications

SPOOK: A system for probabilistic object-oriented knowledge representation (1999)

by A. Pfeffer, D. Koller, B. Milch, and K. Takusagawa


Abstract: 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, Object-oriented Bayesian Networks, 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.


Download Information

A. Pfeffer, D. Koller, B. Milch, and K. Takusagawa (1999). "SPOOK: A system for probabilistic object-oriented knowledge representation." Proceedings of the 15th Annual Conference on Uncertainty in AI (UAI) (pp. 541-550). pdf ps.gz

Bibtex citation

@inproceedings{Pfeffer+al:UAI99,
  author =       "A. Pfeffer and D. Koller and B. Milch and K. Takusagawa",
  booktitle =    "Proceedings of the 15th Annual Conference on
                 Uncertainty in AI (UAI)",
  title =        "{SPOOK}: {A} system for probabilistic object-oriented
                 knowledge representation",
  pages =        "541--550",
  year =         "1999",
}

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