Logo PTI
Polish Information Processing Society
Logo FedCSIS

Annals of Computer Science and Information Systems, Volume 8

Proceedings of the 2016 Federated Conference on Computer Science and Information Systems

A connectionist approach to abductive problems: employing a learning algorithm

, ,

DOI: http://dx.doi.org/10.15439/2016F484

Citation: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 8, pages 353362 ()

Full text

Abstract. This paper presents preliminary results of an application of artificial neural networks and Backpropagation learning algorithm to solve logical abductive problems. To represent logic programs in the form of artificial neural networks CIL2P approach proposed by Garcez et al. is employed. Our abductive procedure makes use of translation of a logic program representing a knowledge base into a neural network, training of the neural network with an example representing an abductive goal and translation of the trained network back to the form of a logic program. An abductive hypothesis is represented as the symmetric difference between the initial logic program and the one obtained after training of the network. The first part of the paper introduces formal description of the tools used to model the abductive process, while the second part illustrates our contribution with results of a few computational experiments and discusses the ways of possible improvements of the proposed procedure.

References

  1. Atocha Aliseda. Abductive Reasoning. Logical Investigations into Discovery and Explanation. Springer, Dordrecht, 2006. http://dx.doi.org/10.1007/1-4020-3907-7.
  2. Dov M. Gabbay and John Woods. The Reach of Abduction. Insight and Trial. Elsevier, 2005. doi: 10.1016/S1874-5075(05)80034-8.
  3. Artur S. d’Avila Garcez, Krysia Broda, and Dov M. Gabbay. Neural-symbolic learning systems: foundations and applications. Springer Science & Business Media, 2002. http://dx.doi.org/10.1007/978-1-4471-0211-3.
  4. Artur S d’Avila Garcez, Dov M Gabbay, Oliver Ray, and John Woods. Abductive reasoning in neural-symbolic systems. Topoi, 26(1):37–49, 2007. http://dx.doi.org/10.1007/s11245-006-9005-5.
  5. Peter Gärdenfors. Belief Revision. Tracts in Theoretical Computer Science 29. Cambridge University Press, 2003. http://dx.doi.org/10.1017/CBO9780511526664.
  6. Jaakko Hintikka. Abduction — inference, conjecture, or an answer to a question? In Socratic Epistemology. Explorations of Knowledge-Seeking by Questioning, pages 38–60. Cambridge University Press, 2007. http://dx.doi.org/10.1017/CBO9780511619298.003.
  7. Tarun Kumar Jain, Dharmender Singh Kushwaha, and Arun Kumar Misra. Optimization of the quine-mccluskey method for the minimization of the boolean expressions. In Fourth International Conference on Autonomic and Autonomous Systems (ICAS’08), pages 165–168. IEEE, 2008. http://dx.doi.org/10.1109/ICAS.2008.11.
  8. M. Komosinski, A. Kups, and M. Urbański. Multicriteria evaluation of abductive hypotheses: towards efficient optimization in proof theory. In Proceedings of the 18th International Conference on Soft Computing, pages 320–325, Brno, Czech Republic, 2012.
  9. M. Komosinski, A. Kups, D. Leszczyńska-Jasion, and M. Urbański. Identifying efficient abductive hypotheses using multi-criteria dominance relation. ACM Transactions on Computational Logic, 15(4), 2014. doi: 10.1145/2629669.
  10. Maciej Komosinski and Szymon Ulatowski. Framsticks web site, 2016. http://www.framsticks.com.
  11. John Wylie Lloyd. Foundations of logic programming. 1993. http://dx.doi.org/10.1007/978-3-642-83189-8.
  12. Edward J McCluskey. Minimization of boolean func- tions. Bell system technical Journal, 35(6):1417–1444, 1956. http://dx.doi.org/10.1002/j.1538-7305.1956.tb03835.x.
  13. Noel Pérez, Miguel Angel Guevara, Augusto Silva, Isabel Ramos, and Joana Loureiro. Improving the performance of machine learning classifiers for breast cancer
  14. diagnosis based on feature selection. In M. Paprzycki M. Ganzha, L. Maciaszek, editor, Proceedings of the 2014 Federated Conference on Computer Science and Information Systems, volume 2 of Annals of Computer Science and Information Systems, pages 209–217. IEEE, 2014. http://dx.doi.org/10.15439/2014F249. URL http://dx.doi.org/10.15439/2014F249.
  15. P. Thagard. Abductive inference: From philosophical analysis to neural mechanisms. In A. Feeney and E. Heit, editors, Inductive reasoning: Cognitive, mathematical, and neuroscientific approaches, pages 226–247. Cambridge University Press, Cambridge, 2007. doi: 10.1017/cbo9780511619304.010. Agnieszka Wosiak and Danuta Zakrzewska. On integrating clustering and statistical analysis for supporting cardiovascular disease diagnosis. In M. Ganzha, L. Maciaszek, and M. Paprzycki, editors, Proceedings of the 2015 Federated Conference on Computer Science and Information Systems, volume 5 of Annals of Computer Science and Information Systems, pages 303–310. IEEE, 2015. http://dx.doi.org/10.15439/2015F151.