SPOOK

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, 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.

This software is also peer reviewed by journal TOMS.


References in zbMATH (referenced in 13 articles )

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  1. Zhang, Haoyuan; Marsh, D. William R.: Multi-state deterioration prediction for infrastructure asset: learning from uncertain data, knowledge and similar groups (2020)
  2. Bach, Stephen H.; Broecheler, Matthias; Huang, Bert; Getoor, Lise: Hinge-loss Markov random fields and probabilistic soft logic (2017)
  3. Kimmig, Angelika; Mihalkova, Lilyana; Getoor, Lise: Lifted graphical models: a survey (2015)
  4. Howard, Catherine; Stumptner, Markus: A survey of directed entity-relation-based first-order probabilistic languages (2014)
  5. Torti, Lionel; Gonzales, Christophe; Wuillemin, Pierre-Henri: Speeding-up structured probabilistic inference using pattern mining (2013)
  6. Brafman, Ronen I.: Relational preference rules for control (2011) ioport
  7. Gonzales, Christophe; Wuillemin, Pierre-Henri: PRM inference using Jaffray & Faÿ’s local conditioning (2011)
  8. Lu, Tsai-Ching; Druzdzel, Marek J.: Interactive construction of graphical decision models based on causal mechanisms (2009)
  9. Getoor, Lise; Grant, John: PRL: A probabilistic relational language (2006) ioport
  10. Lueders, P.: Scene interpretation using Bayesian network fragments (2006)
  11. Town, Christopher: Ontological inference for image and video analysis (2006) ioport
  12. Zapata-Rivera, Diego: cbCPT: Knowledge engineering support for CPTs in Bayesian networks (2002)
  13. Gunderson, James P.; Martin, W. N.: The effects of uncertainty on plan success in a simulated maintenance robot domain. (2000)