Encyclopedia of Machine Learning

2010 Edition
| Editors: Claude Sammut, Geoffrey I. Webb

Abduction

  • Antonis C. Kakas
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-30164-8_1
  • 1.4k Downloads

Definition

Abduction is a form of reasoning, sometimes described as “deduction in reverse,” whereby given a rule that “AfollowsfromB” and the observed result of “A” we infer the condition “B” of the rule. More generally, given a theory, T, modeling a domain of interest and an observation, “A, ” we infer a hypothesis “B” such that the observation follows deductively from T augmented with “B. ” We think of “B” as a possible explanation for the observation according to the given theory that contains our rule. This new information and its consequences (or ramifications) according to the given theory can be considered as the result of a (or part of a) learning process based on the given theory and driven by the observations that are explained by abduction. Abduction can be combined with induction in different ways to enhance this learning process.

Motivation and Background

Abduction is, along with induction, a syntheticform of reasoning whereby it generates, in its explanations, new...

This is a preview of subscription content, log in to check access.

Recommended Reading

  1. Ade, H., & Denecker, M. (1995). AILP: Abductive inductive logic programming. In C. S. Mellish (Ed.), IJCAI (pp. 1201–1209). San Francisco: Morgan Kaufmann.Google Scholar
  2. Ade, H., Malfait, B., & Raedt, L. D. (1994). Ruth: An ILP theory revision system. In ISMIS94. Berlin: Springer.Google Scholar
  3. Alrajeh, D., Ray, O., Russo, A., & Uchitel, S. (2009). Using abduction and induction for operational requirements elaboration. Journal of Applied Logic7(3), 275–288.zbMATHMathSciNetGoogle Scholar
  4. DeJong, G., & Mooney, R. (1986). Explanation-based learning: An alternate view. Machine Learning1, 145–176.Google Scholar
  5. Doncescu, A., Inoue, K., &  Yamamoto, Y. (2007). Knowledge based discovery in systems biology using cf-induction. In H. G. Okuno & M. Ali (Eds.), IEA/AIE (pp. 395–404). Heidelberg: Springer.Google Scholar
  6. Flach, P., &  Kakas, A. (2000). Abductive and inductive reasoning: Background and issues. In P. A. Flach & A. C. Kakas (Eds.), Abductive and inductive reasoning. Pure and applied logic. Dordrecht: Kluwer.Google Scholar
  7. Flach, P. A., & Kakas, A. C. (Eds.). (2009). Abduction and induction in artificial intelligence [Special issue]. Journal of Applied Logic, 7(3).Google Scholar
  8. Inoue, K. (2001). Inverse entailment for full clausal theories. In LICS-2001 workshop on logic and learning.Google Scholar
  9. Ito, K., & Yamamoto, A. (1998). Finding hypotheses from examples by computing the least generlisation of bottom clauses. In Proceedings of discovery science ’98 (pp. 303–314). Berlin: Springer.Google Scholar
  10. Josephson, J., & Josephson, S. (Eds.). (1994). Abductive inference: Computation, philosophy, technology. New York: Cambridge University Press.zbMATHGoogle Scholar
  11. Kakas, A., Kowalski, R., & Toni, F. (1992). Abductive logic programming. Journal of Logic and Computation2(6), 719–770.zbMATHMathSciNetGoogle Scholar
  12. Kakas, A., &  Riguzzi, F. (2000). Abductive concept learning. New Generation Computing18, 243–294.Google Scholar
  13. King, R., Whelan, K., Jones, F., Reiser, P., Bryant, C., Muggleton, S., et al. (2004). Functional genomic hypothesis generation and experimentation by a robot scientist. Nature427, 247–252.Google Scholar
  14. Leake, D. (1995). Abduction, experience and goals: A model for everyday abductive explanation. The Journal of Experimental and Theoretical Artificial Intelligence7, 407–428.Google Scholar
  15. Michalski, R. S. (1993). Inferential theory of learning as a conceptual basis for multistrategy learning. Machine Learning11, 111–151.MathSciNetGoogle Scholar
  16. Moyle, S. (2002). Using theory completion to learn a robot navigation control program. In Proceedings of the 12th international conference on inductive logic programming (pp. 182–197). Berlin: Springer.Google Scholar
  17. Moyle, S. A. (2000). An investigation into theory completion techniques in inductive logic programming. PhD thesis, Oxford University Computing Laboratory, University of Oxford.Google Scholar
  18. Muggleton, S. (1995). Inverse entailment and Progol. New Generation Computing13, 245–286.Google Scholar
  19. Muggleton, S., &  Bryant, C. (2000). Theory completion using inverse entailment. In Proceedings of the tenth international workshop on inductive logic programming (ILP-00) (pp. 130–146). Berlin: Springer.Google Scholar
  20. Ourston, D., & Mooney, R. J. (1994). Theory refinement combining analytical and empirical methods. Artificial Intelligence66, 311–344.MathSciNetGoogle Scholar
  21. Papatheodorou, I., Kakas, A., & Sergot, M. (2005). Inference of gene relations from microarray data by abduction. In Proceedings of the eighth international conference on logic programming and non-monotonic reasoning (LPNMR’05) (Vol. 3662, pp. 389–393). Berlin: Springer.Google Scholar
  22. Ray, O. (2009). Nonmonotonic abductive inductive learning. Journal of Applied Logic7(3), 329–340.zbMATHMathSciNetGoogle Scholar
  23. Ray, O., Antoniades, A., Kakas, A., & Demetriades, I. (2006). Abductive logic programming in the clinical management of HIV/AIDS. In G. Brewka, S. Coradeschi, A. Perini, & P. Traverso (Eds.), Proceedings of the 17th European conference on artificial intelligence. Frontiers in artificial intelligence and applications (Vol. 141, pp. 437–441). Amsterdam: IOS Press.Google Scholar
  24. Ray, O., Broda, K., & Russo, A. (2003). Hybrid abductive inductive learning: A generalisation of Progol. In Proceedings of the 13th international conference on inductive logic programming. Lecture notes in artificial intelligence (Vol. 2835, pp. 311–328). Berlin: Springer.Google Scholar
  25. Ray, O., & Bryant, C. (2008). Inferring the function of genes from synthetic lethal mutations. In Proceedings of the second international conference on complex, intelligent and software intensive systems (pp. 667–671). Washington, DC: IEEE Computer Society.Google Scholar
  26. Ray, O., Flach, P. A., & Kakas, A. C. (Eds.). (2009). Abduction and induction in artificial intelligence. Proceedings of IJCAI 2009 workshop.Google Scholar
  27. Reggia, J. (1983). Diagnostic experts systems based on a set-covering model. International Journal of Man-Machine Studies19(5), 437–460.Google Scholar
  28. Tamaddoni-Nezhad, A., Chaleil, R., Kakas, A., & Muggleton, S. (2006). Application of abductive ILP to learning metabolic network inhibition from temporal data. Machine Learning64(1–3), 209–230.zbMATHGoogle Scholar
  29. Tamaddoni-Nezhad, A., Kakas, A., Muggleton, S., & Pazos, F. (2004). Modelling inhibition in metabolic pathways through abduction and induction. In Proceedings of the 14th international conference on inductive logic programming (pp. 305–322). Berlin: Springer.Google Scholar
  30. Yamamoto, A. (1997). Which hypotheses can be found with inverse entailment? In Proceedings of the seventh international workshop on inductive logic programming. Lecture notes in artificial intelligence (Vol. 1297, pp. 296–308). Berlin: Springer.Google Scholar
  31. Zupan, B., Bratko, I., Demsar, J., Juvan, P., Halter, J., Kuspa, A., et al. (2003). Genepath: A system for automated construction of genetic networks from mutant data. Bioinformatics19(3), 383–389.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Antonis C. Kakas

There are no affiliations available