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Representation and Reasoning for Recursive Probability Models

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Book cover AI 2006: Advances in Artificial Intelligence (AI 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4304))

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Abstract

This paper applies the Object Oriented Probabilistic Relational Modelling Language to recursive probability models. We present two novel anytime inference algorithms for recursive probability models expressed using this language. We discuss the strengths and limitations of these algorithms and compare their performance against the Iterative Structured Variable Elimination algorithm proposed for Probabilistic Relational Modelling Language using three different non-linear genetic recursive probability models.

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References

  1. Pfeffer, A., Koller, D.: Semantics and Inference for Recursive Probability Models. In: National Conference on Artificial Intelligence (AAAI) (2000)

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  2. Pfeffer, A.J.: Probabilistic Reasoning for Complex Systems, PhD thesis in Department of Computer Science. Stanford University (1999)

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  3. Howard, C., Stumpter, M.: Situation Assessments Using Object Oriented Probabilistic Relational Models. In: Proc. 8th Int’l. Conf. on Information Fusion. Philadelphia (2005)

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  4. Howard, C., Stumptner, M.: Model Construction Algorithms for Object Oriented Probabilistic Relational Models. In: 19th Int’l. Florida Artificial Intelligence Research Society’s Conf., Florida (2006)

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Author information

Authors and Affiliations

  1. Electronic Warfare and Radar Division, Defence Science and Technology Organisation, PO Box 1500, Edinburgh, South Australia, 5111

    Catherine Howard

  2. Advanced Computing Research Center, University of South Australia, Adelaide, 5095

    Markus Stumptner

Authors
  1. Catherine Howard
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  2. Markus Stumptner
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Editor information

Editors and Affiliations

  1. DisPRR, National ICT Australia Ltd, QLD, Australia

    Abdul Sattar

  2. School of Computing, University of Tasmania, Sandy Bay, 7005, Tasmania, Australia

    Byeong-ho Kang

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© 2006 Springer-Verlag Berlin Heidelberg

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Howard, C., Stumptner, M. (2006). Representation and Reasoning for Recursive Probability Models. In: Sattar, A., Kang, Bh. (eds) AI 2006: Advances in Artificial Intelligence. AI 2006. Lecture Notes in Computer Science(), vol 4304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11941439_16

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  • DOI: https://doi.org/10.1007/11941439_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-49787-5

  • Online ISBN: 978-3-540-49788-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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