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.
     [Expand to show all 19][Minimize]
Plan Recognition Using Statistical Relational Models 2014
Sindhu Raghavan, Parag Singla, and Raymond J. Mooney, In Plan, Activity, and Intent Recognition: Theory and Practice, Sukthankar, G. and Geib, C. and Bui, H.H. and Pynadath, D. and Goldman, R.P. (Eds.), pp. 57--85, Burlington, MA 2014. Morgan Kauf...
Bayesian Logic Programs for Plan Recognition and Machine Reading 2012
Sindhu Raghavan, PhD Thesis, Department of Computer Science, University of Texas at Austin. 170.
Abductive Markov Logic for Plan Recognition 2011
Parag Singla and Raymond J. Mooney, Proceedings of the 25th AAAI Conference on Artificial Intelligence (AAAI-2011) (2011), pp. 1069-1075.
Abductive Plan Recognition by Extending Bayesian Logic Programs 2011
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), Vol. 2, pp. 629-644, September 2011.
Extending Bayesian Logic Programs for Plan Recognition and Machine Reading 2011
Sindhu V. Raghavan, Technical Report, PhD proposal, Department of Computer Science, The University of Texas at Austin.
Implementing Weighted Abduction in Markov Logic 2011
James Blythe, Jerry R. Hobbs, Pedro Domingos, Rohit J. Kate, Raymond J. Mooney, In Proceedings of the International Conference on Computational Semantics, pp. 55--64, Oxford, England, January 2011.
Bayesian Abductive Logic Programs 2010
Sindhu Raghavan and Raymond Mooney, In Proceedings of the AAAI-10 Workshop on Statistical Relational AI (Star-AI 10), pp. 82--87, Atlanta, GA, July 2010.
Probabilistic Abduction using Markov Logic Networks 2009
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.
Integrating Abduction and Induction in Machine Learning 2000
Raymond J. Mooney, In Abduction and Induction, P. A. Flach and A. C. Kakas (Eds.), pp. 181-191 2000. Kluwer Academic Publishers.
Integrating Abduction and Induction in Machine Learning 1997
Raymond J. Mooney, In Working Notes of the IJCAI-97 Workshop on Abduction and Induction in AI, pp. 37--42, Nagoya, Japan, August 1997.
Inductive Learning For Abductive Diagnosis 1994
Cynthia A. Thompson and Raymond J. Mooney, In Proceedings of the Twelfth National Conference on Artificial Intelligence (AAAI-94), pp. 664-669, Seattle, WA, August 1994.
Inductive Learning For Abductive Diagnosis 1993
Cynthia A. Thompson, Masters Thesis, Department of Computer Sciences, The University of Texas at Austin. 53 pages.
A First-Order Horn-Clause Abductive System and Its Use in Plan Recognition and Diagnosis 1992
Hwee Tou Ng and Raymond J. Mooney, unpublished. Unpublished Technical Note.
A General Abductive system with application to plan recognition and diagnosis 1992
Hwee Tou Ng, PhD Thesis, Department of Computer Sciences, University of Texas at Austin. 154 pages.
Abductive Plan Recognition and Diagnosis: A Comprehensive Empirical Evaluation 1992
Hwee Tou Ng and Raymond J. Mooney, In Proceedings of the Third International Conference on Principles of Knowledge Representation and Reasoning, pp. 499--508, Cambridge, MA, October 1992.
Automatic Abduction of Qualitative Models 1992
Bradley L. Richards, Ina Kraan, and Benjamin J. Kuipers, In Proceedings of the Fifth International Workshop on Qualitative Reasoning about Physical Systems, pp. 295-301 1992.
Belief Revision in the Context of Abductive Explanation 1992
Siddarth Subramanian, Technical Report AI92-179, Artificial Intelligence Laboratory, University of Texas.
An Efficient First-Order Horn-Clause Abduction System Based on the ATMS 1991
Hwee Tou Ng and Raymond J. Mooney, In Proceedings of the Ninth National Conference on Artificial Intelligence (AAAI-91), pp. 494-499, Anaheim, CA, July 1991.
On the Role of Coherence in Abductive Explanation 1990
Hwee Tou Ng and Raymond J. Mooney, In Proceedings of the Eighth National Conference on Artificial Intelligence (AAAI-90), pp. 337--342, Boston, MA, July 1990.