Department of Computer Science

Machine Learning Research Group

University of Texas at Austin Artificial Intelligence Lab

Publications: 1993


Show abstracts
  1. Extending Theory Refinement to M-of-N Rules
    [Details] [PDF]
    Paul T. Baffes and Raymond J. Mooney
    Informatica, 17:387-397, 1993.
  2. Inductive Learning For Abductive Diagnosis
    [Details] [PDF]
    Cynthia A. Thompson
    Masters Thesis, Department of Computer Sciences, The University of Texas at Austin, Austin, TX, August 1993. 53 pages.
  3. Combining FOIL and EBG to Speed-Up Logic Programs
    [Details] [PDF]
    John M. Zelle and Raymond J. Mooney
    In Proceedings of the 13th International Joint Conference on Artificial Intelligence, 1106-1111, 1993. San Francisco, CA: Morgan Kaufmann.
  4. Symbolic Revision of Theories With M-of-N Rules
    [Details] [PDF]
    Paul T. Baffes and Raymond J. Mooney
    In Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence (IJCAI-93), 1135-1140, Chambery, France, August 1993.
  5. Learning Semantic Grammars With Constructive Inductive Logic Programming
    [Details] [PDF]
    John M. Zelle and Raymond J. Mooney
    In Proceedings of the 11th National Conference on Artificial Intelligence, 817-822, 1993. Menlo Park, CA: AAAI Press.
  6. Learning to Model Students: Using Theory Refinement to Detect Misconceptions
    [Details] [PDF]
    Paul T. Baffes
    June 1993. Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin.
  7. Combining Connectionist and Symbolic Learning to Refine Certainty-Factor Rule-Bases
    [Details] [PDF]
    J. Jeffrey Mahoney and Raymond J. Mooney
    Connection Science:339-364, 1993.
  8. Integrating Theory and Data in Category Learning
    [Details] [PDF]
    Raymond J. Mooney
    In G. V. Nakamura and D. L. Medin and R. Taraban, editors, Categorization by Humans and Machines, 189-218, 1993.
  9. Learning Search-Control Heuristics for Logic Programs: Applications to Speedup Learning and Language Acquisition
    [Details] [PDF]
    John M. Zelle
    March 1993. Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin.
  10. Induction Over the Unexplained: Using Overly-General Domain Theories to Aid Concept Learning
    [Details] [PDF]
    Raymond J. Mooney
    Machine Learning, 10:79-110, 1993.