UT ML Group: Uncertain Reasoning

Uncertain reasoning in AI concerns various forms of "probabilistic" as opposed to logical inference. Our work in the area has focused on
theory refinement for knowledge bases using uncertainty such as certainty-factor rule bases and Bayesian networks. Rather than building such knowledge-bases completely manually or learning them form scratch, our work focuses on using data to revise imperfect expert-supplied initial KB's.

Publications

  1. Discriminative Structure and Parameter Learning for Markov Logic Networks [Abstract] [PDF]
    Tuyen N. Huynh and Raymond J. Mooney
    In Proceedings of the 25th International Conference on Machine Learning (ICML) , Helsinki, Finland, July 2008.

  2. Bottom-Up Learning of Markov Logic Network Structure [Abstract] [PDF]
    Lilyana Mihalkova and Raymond J. Mooney
    In Proceedings of the 24th Annual International Conference on Machine Learning (ICML-2007), Corvallis, OR, June 2007.

  3. Theory Refinement for Bayesian Networks with Hidden Variables [Abstract] [PDF]
    Sowmya Ramachandran and Raymond J. Mooney
    Proceedings of the Fifteenth International Conference on Machine Learning (ICML-98), Madison, WI, pp. 454-462, July 1998.

  4. Theory Refinement of Bayesian Networks with Hidden Variables [Abstract] [PDF]
    Sowmya Ramachandran and Raymond J. Mooney
    Ph.D. Thesis, Department of Computer Sciences, University of Texas at Austin, May 1998.
    139 pages
    Also appears as Technical Report AI 98-265, Artificial Intelligence Lab, University of Texas at Austin.

  5. Combining Symbolic and Connectionist Learning Methods to Refine Certainty-Factor Rule-Bases [Abstract] [PDF]
    J. Jeffrey Mahoney
    Ph.D. Thesis, Department of Computer Sciences, University of Texas at Austin, May 1996.
    113 pages

  6. Revising Bayesian Network Parameters Using Backpropagation [Abstract] [PDF]
    Sowmya Ramachandran and Raymond J. Mooney
    Proceedings of the International Conference on Neural Networks (ICNN-96), Special Session on Knowledge-Based Artificial Neural Networks, pp. 82-87, Washington DC, June 1996.

  7. Refinement of Bayesian Networks by Combining Connectionist and Symbolic Techniques [Abstract] [PDF]
    Sowmya Ramachandran
    Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin, October 1995.
    34 pages

  8. Comparing Methods For Refining Certainty Factor Rule-Bases [Abstract] [PDF]
    J. Jeffrey Mahoney and Raymond J. Mooney
    Proceedings of the Eleventh International Workshop on Machine Learning (ML-94), pp. 173-180, Rutgers, NJ, July 1994.

  9. Modifying Network Architectures For Certainty-Factor Rule-Base Revision [Abstract] [PDF]
    J. Jeffrey Mahoney and Raymond J. Mooney
    Proceedings of the International Symposium on Integrating Knowledge and Neural Heuristics 1994 (ISIKNH-94), pp. 75-85, Pensacola, FL, May 1994.

  10. Combining Connectionist and Symbolic Learning to Refine Certainty-Factor Rule-Bases [Abstract] [PDF]
    J. Jeffrey Mahoney and Raymond J. Mooney
    Connection Science, 5 (1993), pp. 339-364. (Special issue on Architectures for Integrating Neural and Symbolic Processing)

  11. Combining Symbolic and Neural Learning to Revise Probabilistic Theories [Abstract] [PDF]
    J. Jeffrey Mahoney and Raymond J. Mooney
    Proceedings of the 1992 Machine Learning Workshop on Integrated Learning in Real Domains, Aberdeen, Scotland, July 1992.

mooney@cs.utexas.edu