JACIII Vol.15 No.8 pp. 954-961
doi: 10.20965/jaciii.2011.p0954


Abductive Reasoning as an Integrating Framework in Skill Acquisition

Koichi Furukawa*, Toshiki Masuda**, and Ikuo Kobayashi***

*Graduate School of Business Innovation, Kaetsu University


***SFC Research Institute, Keio University

February 21, 2011
January 1, 1970
October 20, 2011
abduction, skill acquisition, rule abduction, surprising facts, reasoning diagram
Skill acquisition includes many issues, such as finding explanations for skillful performance, finding missing links in skill explanations, diagnosing performance malfunctions, identifying the role of “surprising facts” in skill discovery, and accommodating new skills. Interestingly, most of these issues are treated appropriately in an abductive reasoning framework. In this article, we focus on the skill of cello playing. A cellistmay find musical passages not playable using previously acquired methods alone. In such a case, we must invent a new skill, which we call an abduced skill. A simple case of skill abduction can be realized by adding a factual hypothesis, whereas in some cases we need rule abduction. In acquiring new skills, an instructor’s suggestion may improve a learner’s skill. This indicates the importance of such suggestions, which are called “surprising facts” in abduction. We point out the importance of surprising facts by showing two examples of dramatic improvement.
Cite this article as:
K. Furukawa, T. Masuda, and I. Kobayashi, “Abductive Reasoning as an Integrating Framework in Skill Acquisition,” J. Adv. Comput. Intell. Intell. Inform., Vol.15 No.8, pp. 954-961, 2011.
Data files:
  1. [1] A. C. Kakas, R. A. Kowalski, and F. Toni, “The role of abduction in logic programming. Handbook of logic in Artificial Intelligence and Logic Programming,” Vol.5, Oxford University Press, pp. 235-324, 1998.
  2. [2] C. S. Peirce, “Collected papers of Charles Sanders Peirce,” Hartshorn et al. (Eds.), Vol.2, Harvard University Press, pp. 1931-1958.
  3. [3] K. Furukawa (Ed.), “Introduction to Skill Science,” Japanese Society for Artificial Intelligence, Ohm-sha, 2009 (in Japanese).
  4. [4] O. Ray and A.C. Kakas, “ProLogICA: a practical system for Abductive Logic Programming,” Proc. 11th Non Monotonic Reasoning Workshop, pp. 304-314, 2006.
  5. [5] K. Furukawa, “Skill Science,” J. Japanese Society for Artificial Intelligence, Vol.19, No.3, pp. 355-364, 2004 (in Japanese).
  6. [6] K. Inoue, K. Furukawa, and I. Kobayashi, “Abducing Rules with Predicate Invention,” ILP 2009, Leuven, Belgium, 2009, An extended version is to appear as a selected paper in: Luc De Raedt (ed.), Post-Proc. 19th Int. Conf. Inductive Logic Programming, LNAI, Springer, 2009.
  7. [7] H. Nabeshima, K. Iwanuma, and K. Inoue, “SOLAR: A Consequence Finding System for Advanced Reasoning,” Proc. of 11th International Conference (TABLEAUX 2003), Lecture Notes in Computer Science, Vol. 2796, pp. 257-263, Springer, 2003.
  8. [8] K. Inoue, “Linear Resolution for Consequence Finding. Artificial Intelligence,” Vol.56, No.2-3, pp. 301-353, 1992.
  9. [9] K. Furukawa, K. Inoue, I. Kobayashi, and M. Suwa, “Discovering Knack by Abductive Reasoning,” SIG-SKL (Skill Science), 03-03, Japanese Society for Artificial Intelligence, 2009 (in Japanese).
  10. [10] N. Everaert-Desmedt, “Peirce’s Esthetics,” in Louis Hebert (dir.), Signo
  11. [11] [online], Rimouski (Quebec), 2006.
  12. [12] J. Piaget, “Six etudes de psychologie,” Folio essays, 1964.
  13. [13] I. Kobayashi,. and K. Furukawa, “Modeling physical skill discovery and diagnosis by abduction,” Tran. Japanese Society for Artificial Intelligence, Vol.23, No.3, pp. 127-140, 2008.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jul. 23, 2024