Department of Computer Science

Machine Learning Research Group

University of Texas at Austin Artificial Intelligence Lab

Publications: Abduction

Abduction is inference to the best explanation and has applications to diagnosis, plan recognition, natural language understanding, vision, and many other tasks. It is frequently formalized as constructing a set of assumptions that logically imply and therefore "explain" a set of observations. Our work in abduction has focused on efficient logical abduction systems using truth maintenance techniques, applications of abduction to identifying faults in the process of theory refinement, and the induction of knowledge bases suitable for abductive reasoning. Our current research focuses on using statistical relational learning for abductive reasoning.

Below is an extreme example of abduction from Eugene Ionesco's play `Rhinoceros' from the `Theater of the Absurd' school:

All cats die.
Socrates is dead.
Therefore, Socrates is a cat.
  1. Plan Recognition Using Statistical Relational Models
    [Details] [PDF]
    Sindhu Raghavan and Parag Singla and Raymond J. Mooney
    In Sukthankar, G. and Geib, C. and Bui, H.H. and Pynadath, D. and Goldman, R.P., editors, Plan, Activity, and Intent Recognition: Theory and Practice, 57--85, Burlington, MA, 2014. Morgan Kaufmann.
  2. Bayesian Logic Programs for Plan Recognition and Machine Reading
    [Details] [PDF] [Slides]
    Sindhu Raghavan
    PhD Thesis, Department of Computer Science, University of Texas at Austin, December 2012. 170.
  3. Abductive Plan Recognition by Extending Bayesian Logic Programs
    [Details] [PDF] [Slides]
    Sindhu Raghavan, Raymond J. Mooney
    In Proceedings of the European Conference on Machine Learning/Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2011), 629-644, September 2011.
  4. Abductive Markov Logic for Plan Recognition
    [Details] [PDF] [Slides]
    Parag Singla and Raymond J. Mooney
    In Proceedings of the 25th AAAI Conference on Artificial Intelligence (AAAI-2011), 1069-1075, August 2011.
  5. Extending Bayesian Logic Programs for Plan Recognition and Machine Reading
    [Details] [PDF] [Slides]
    Sindhu V. Raghavan
    Technical Report, PhD proposal, Department of Computer Science, The University of Texas at Austin, May 2011.
  6. Implementing Weighted Abduction in Markov Logic
    [Details] [PDF]
    James Blythe, Jerry R. Hobbs, Pedro Domingos, Rohit J. Kate, Raymond J. Mooney
    In Proceedings of the International Conference on Computational Semantics, 55--64, Oxford, England, January 2011.
  7. Bayesian Abductive Logic Programs
    [Details] [PDF] [Slides]
    Sindhu Raghavan and Raymond Mooney
    In Proceedings of the AAAI-10 Workshop on Statistical Relational AI (Star-AI 10), 82--87, Atlanta, GA, July 2010.
  8. Probabilistic Abduction using Markov Logic Networks
    [Details] [PDF] [Slides]
    Rohit J. Kate and Raymond J. Mooney
    In Proceedings of the IJCAI-09 Workshop on Plan, Activity, and Intent Recognition (PAIR-09), Pasadena, CA, July 2009.
  9. Integrating Abduction and Induction in Machine Learning
    [Details] [PDF]
    Raymond J. Mooney
    In P. A. Flach and A. C. Kakas, editors, Abduction and Induction, 181-191, 2000. Kluwer Academic Publishers.
  10. Integrating Abduction and Induction in Machine Learning
    [Details] [PDF]
    Raymond J. Mooney
    In Working Notes of the IJCAI-97 Workshop on Abduction and Induction in AI, 37--42, Nagoya, Japan, August 1997.
  11. Inductive Learning For Abductive Diagnosis
    [Details] [PDF]
    Cynthia A. Thompson and Raymond J. Mooney
    In Proceedings of the Twelfth National Conference on Artificial Intelligence (AAAI-94), 664-669, Seattle, WA, August 1994.
  12. 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.
  13. A General Abductive system with application to plan recognition and diagnosis
    [Details] [PDF]
    Hwee Tou Ng
    PhD Thesis, Department of Computer Sciences, University of Texas at Austin, May 1992. 154 pages.
  14. Abductive Plan Recognition and Diagnosis: A Comprehensive Empirical Evaluation
    [Details] [PDF]
    Hwee Tou Ng and Raymond J. Mooney
    In Proceedings of the Third International Conference on Principles of Knowledge Representation and Reasoning, 499--508, Cambridge, MA, October 1992.
  15. Automatic Abduction of Qualitative Models
    [Details] [PDF]
    Bradley L. Richards, Ina Kraan, and Benjamin J. Kuipers
    In Proceedings of the Fifth International Workshop on Qualitative Reasoning about Physical Systems, 295-301, 1992.
  16. A First-Order Horn-Clause Abductive System and Its Use in Plan Recognition and Diagnosis
    [Details] [PDF]
    Hwee Tou Ng and Raymond J. Mooney
    June 1992. Unpublished Technical Note.
  17. Belief Revision in the Context of Abductive Explanation
    [Details] [PDF]
    Siddarth Subramanian
    Technical Report AI92-179, Artificial Intelligence Laboratory, University of Texas, Austin, TX, December 1992.
  18. An Efficient First-Order Horn-Clause Abduction System Based on the ATMS
    [Details] [PDF]
    Hwee Tou Ng and Raymond J. Mooney
    In Proceedings of the Ninth National Conference on Artificial Intelligence (AAAI-91), 494-499, Anaheim, CA, July 1991.
  19. On the Role of Coherence in Abductive Explanation
    [Details] [PDF]
    Hwee Tou Ng and Raymond J. Mooney
    In Proceedings of the Eighth National Conference on Artificial Intelligence (AAAI-90), 337--342, Boston, MA, July 1990.