Department of Computer Science

Machine Learning Research Group

University of Texas at Austin Artificial Intelligence Lab

Publications: Learning for Planning and Problem Solving

Most learning research concerns classification. Research in learning and planning and problem solving focuses on improving the performance of an AI planning or problem solving system through experience. Our work has focussed on integrating explanation-based learning (EBL) and inductive learning (specifically ILP) to improve the efficiency (speedup learning) and solution-quality for planning and problem solving systems by solving sample problems and learning heuristics that avoid backtracking or sub-optimal solutions.

Our work has focused on two systems:

  • SCOPE: Learning control rules for partial-order planning to improve efficiency and plan quality
  • DOLPHIN: Learning clause-selection rules for dynamic optimization of logic programs
  1. Using Multi-Strategy Learning to Improve Planning Efficiency and Quality
    [Details] [PDF]
    Tara A. Estlin
    PhD Thesis, Department of Computer Sciences, The University of Texas at Austin, Austin, TX, 1998.
  2. Learning to Improve both Efficiency and Quality of Planning
    [Details] [PDF]
    Tara A. Estlin and Raymond J. Mooney
    In Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence (IJCAI-97), 1227-1232, Nagoya, Japan, 1997.
  3. Integrating Explanation-Based and Inductive Learning Techniques to Acquire Search-Control for Planning
    [Details] [PDF]
    Tara A. Estlin
    Technical Report AI96-250, Department of Computer Sciences, University of Texas, Austin, TX, 1996.
  4. Multi-Strategy Learning of Search Control for Partial-Order Planning
    [Details] [PDF]
    Tara A. Estlin and Raymond J. Mooney
    In Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI-96), 843-848, Portland, OR, August 1996.
  5. Integrating EBL and ILP to Acquire Control Rules for Planning
    [Details] [PDF]
    Tara A. Estlin and Raymond J. Mooney
    In Proceedings of the Third International Workshop on Multi-Strategy Learning (MSL-96), 271--279, Harpers Ferry, WV, May 1996.
  6. Hybrid Learning of Search Control for Partial-Order Planning
    [Details] [PDF]
    Tara A. Estlin and Raymond J. Mooney
    In Malik Ghallab and Alfredo Milani, editors, New Directions in AI Planning, 129-140, Amsterdam, 1996. IOS Press.
  7. Integrating ILP and EBL
    [Details] [PDF]
    Raymond J. Mooney and John M. Zelle
    Sigart Bulletin (special issue on Inductive Logic Programmming), 5(1):12-21, 1994.
  8. 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.
  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. Speeding-up Logic Programs by Combining EBG and FOIL
    [Details] [PDF]
    John M. Zelle and Raymond J. Mooney
    In Proceedings of the 1992 Machine Learning Workshop on Knowledge Compilation and Speedup Learning, Aberdeen, Scotland, July 1992.
  11. The Effect of Rule Use on the Utility of Explanation-Based Learning
    [Details] [PDF]
    Raymond J. Mooney
    In Proceedings of the 11th International Joint Conference on Artificial Intelligence, 725-730, 1989. San Francisco, CA: Morgan Kaufmann.
  12. Generalizing the Order of Operators in Macro-Operators
    [Details] [PDF]
    Raymond J. Mooney
    In Proceedings of the Fifth International Conference on Machine Learning (ICML-88), 270-283, Ann Arbor, MI, June 1988.