Advertisement

Automated Abduction in Scientific Discovery

  • Oliver Ray
Part of the Studies in Computational Intelligence book series (SCI, volume 64)

Summary. The role of abduction in the philosophy of science has been well studied in recent years and has led to a deeper understanding of many formal and pragmatic issues [1–5]. This paper is written from the point of view that real applications are now needed to help consolidate what has been learned so far and to inspire new developments. With an emphasis on computational mechanisms, it examines the abductive machinery used for generating hypotheses in a recent Robot Scientist project [6] and shows how techniques from Abductive Logic Programming [7] offer superior reasoning capabilities needed in more advanced practical applications. Two classes of abductive proof procedures are identified and compared in a case study. Backward-chaining logic programming methods are shown to outperform theorem proving approaches based on the use of contrapositive reasoning.

Keywords

Logic Program Integrity Constraint Inductive Logic Programming Horn Clause Abductive Reasoning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Magnani, L.: Abduction, Reason, and Science. Processes of Discovery and Expla-nation. Kluwer (2001)Google Scholar
  2. 2.
    Aliseda, A.: Abductive Reasoning: Logical Investigations into Discovery and Explanation. Volume 230 of Synthese Library. Springer (2006)Google Scholar
  3. 3.
    Gabbay, D., Woods, J.: The reach of abduction: Insight and trial. Elsevier (2005)Google Scholar
  4. 4.
    Flach, P., Kakas, A., eds.: Abduction and Induction: essays on their relation and integration. Volume 18 of Applied Logic Series. Kluwer (2000)Google Scholar
  5. 5.
    Josephson, J., (Eds.), S.J.: Abductive inference: computation, philosophy, tech-nology. Cambridge University Press (1994)Google Scholar
  6. 6.
    King, R., Whelan, K., Jones, F., Reiser, P., Bryant, C., Muggleton, S., Kell, D., Oliver, S.: Functional genomic hypothesis generation and experimentation by a robot scientist. Nature 427 (2004) 247-252CrossRefGoogle Scholar
  7. 7.
    Kakas, A., Kowalski, R., Toni, F.: Abductive Logic Programming. Journal of Logic and Computation 2(6) (1992) 719-770zbMATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Hartshorne, C., Weiss, P., Burks, A., eds.: Collected papers of Charles Sanders Peirce. Harvard University Press (1931-1958)Google Scholar
  9. 9.
    King, R., Benway, M.: Robot Scientist: an Autonomous Platform for Systems Biology Discovery. A poster at the 12th Annual Conference and Exhibition of the Society for Biomolecular Sciences (2006)Google Scholar
  10. 10.
    Bryant, C., Muggleton, S., Oliver, S., Kell, D., Reiser, P., King, R.: Combining Inductive Logic Programming, Active Learning and Robotics to Discover the Function of Genes. Electronic Transactions on Artificial Intelligence 5 (Section B) (2001) 1-36Google Scholar
  11. 11.
    Muggleton, S., Bryant, C.: Theory Completion Using Inverse Entailment. In: Proceedings of the 10th International Conference on Inductive Logic Program-ming. Volume 1866 of Lecture Notes in Computer Science. Springer Verlag (2000)130-146Google Scholar
  12. 12.
    Stickel, M.: A Prolog technology theorem prover: A New Exposition and Imple-mentation in Prolog. Theoretical Computer Science 104 (1992) 109-128zbMATHCrossRefMathSciNetGoogle Scholar
  13. 13.
    Ray, O.: Hybrid Abductive-Inductive Learning. PhD thesis, Department of Computing, Imperial College London, UK (2005)Google Scholar
  14. 14.
    Lloyd, J.: Foundations of Logic Programming. Springer Verlag (1987)Google Scholar
  15. 15.
    Muggleton, S.: Inverse Entailment and Progol. New Generation Computing, Special issue on Inductive Logic Programming 13(3-4) (1995) 245-286Google Scholar
  16. 16.
    Jr., H.P.: On The Mechanization of Abductive Logic. In: Proceedings of the 3rd International Joint Conference on Artificial Intelligence, William Kaufmann (1973) 147-152Google Scholar
  17. 17.
    Finger, J., Genesereth, M.: Residue: a deductive approach to design synthesis. Technical Report STAN-CS-85-1035, Stanford University, USA (1985)Google Scholar
  18. 18.
    Stickel, M.: A Prolog-like inference system for computing minimum-cost abduc-tive explanations in natural-language interpretation. Annals of Mathematics and Artificial Intelligence 4 (1991) 89-106zbMATHCrossRefGoogle Scholar
  19. 19.
    Morgan, C.: Hypothesis generation by machine. Artificial Intelligence 2(2) (1971) 179-187zbMATHCrossRefMathSciNetGoogle Scholar
  20. 20.
    Cox, P., Pietrzykowski, T.: Causes for Events: Their computation and Appli-cation. In Siekmann, J., ed.: Proceedings of the 8th International Conference on Automated Deduction. Volume 230 of Lecture Notes in Computer Science., Springer (1986) 608-621Google Scholar
  21. 21.
    Inoue, K.: Linear resolution for consequence finding. Artificial Intelligence 56(2-3) (1992) 301-353zbMATHCrossRefMathSciNetGoogle Scholar
  22. 22.
    Kakas, A., Mancarella, P.: Database Updates through Abduction. In: Pro-ceedings of the 16th International Conference on Very Large Databases, Morgan Kaufmann (1990) 650-661Google Scholar
  23. 23.
    Robinson, J.: A Machine-Oriented Logic based on the Resolution Principle. Journal of the ACM 12(1) (1965) 23-41zbMATHCrossRefGoogle Scholar
  24. 24.
    Ray, O., Kakas, A.: Prologica: a practical system for abductive logic program-ming. In: Dix, J., Hunter, A., eds.: 11th International Workshop on Non-monotonic Reasoning. IFL Technical Report Series, Clausthal University of Technology (2006) 304-312Google Scholar
  25. 25.
    Jacob, F., Monod, J.: Genetic regulatory mechanisms in the synthesis of pro-teins. Journal of Molecular Biology 3 (1961) 318-356CrossRefGoogle Scholar
  26. 26.
    Ray, O., Broda, K., Russo, A.: Hybrid Abductive Inductive Learning: a Gen-eralisation of Progol. In: Horváth, T., Yamamoto, A., eds.: Proceedings of the 13th International Conference on Inductive Logic Programming. Volume 2835 of Lecture Notes in Artificial Intelligence., Springer Verlag (2003) 311-328Google Scholar
  27. 27.
    Ray, O.: The need for Ancestor Resolution when answering queries in Horn clause logic. In: Gabbrielli, M., Gupta, G., eds.: Proceedings of the 21st Inter-national Conference on Logic Programming. Volume 3668 of Lecture Notes in Computer Science., Springer Verlag (2005) 410-411Google Scholar
  28. 28.
    Ray, O.: Using abduction for induction of normal logic programs. In: Proceedings of the ECAI’06 Workshop on Abduction and Induction in Artificial Intelligence and Scientific Modelling. (2006) 28-31Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Oliver Ray
    • 1
  1. 1.Department of Computer ScienceUniversity of CyprusNicosiaCyprus

Personalised recommendations