Skip to main content
SpringerLink
Log in

Argument-Based Logic Programming for Analogical Reasoning

  • Conference paper
  • First Online:
New Frontiers in Artificial Intelligence (JSAI-isAI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10247))

Included in the following conference series:

Abstract

Analogical reasoning can be understood as a kind of resemblance of one thing to another, thus assigning properties from one context to another. The key idea is to use similarity information to support an inference which cannot be deductively inferred. In this paper, we present a formal and intuitive framework of this phenomena using an argument-based logic-programming-like language. A proof theory of our system is stated in the dialectical style, where a proof takes the form of dialogue between a proponent and an opponent of an argument. We also discuss how the proposed framework can be fine tuned for optimistic analogical reasoning and pessimistic analogical reasoning. Finally, we discuss a design sketch of our proposed analogical reasoner called Analogist.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Notes

  1. 1.

    In the original definition of preference profile [11, 12], both \(\mathfrak {i} ^\mathfrak {c} \) and \(\mathfrak {i} ^\mathfrak {r} \) are mapped to \(\mathbb {R}_{\ge 0}\) which is a minor error.

  2. 2.

    When \(x = 1\), we may remove it.

  3. 3.

    When \(w = 1\), we may remove it.

  4. 4.

    The precise definition of t-norm is given later.

  5. 5.

    We may employ the notion of \(\overset{\pi }{\sim }_\mathcal {T} \) to obtain 0.8 from realistic ontologies.

  6. 6.

    Later, this definition is used by Definition 11 for comparing between rebuttal attacks.

  7. 7.

    For the sake of succinctness, \(\cdot ^\mathsf {D}\) (and \(\cdot ^\mathsf {S}\)) indicates a duplicated set of defeasible rules (and similarity rules, respectively) from a specified argument structure.

References

  1. Lief, M.S., Caldwell, H.M., Bycel, B.: Ladies and Gentlemen of the Jury: Greatest Closing Arguments in Modern Law. Scribner, New York (2000)

    Google Scholar 

  2. Hofstadter, D., Mitchell, M.: Concepts, analogies, and creativity. In: Proceedings of CSCSI 1988, pp. 94–101, June 1988

    Google Scholar 

  3. Walton, D., Reed, C., Macagno, F.: Argumentation Schemes. Cambridge University Press, Cambridge (2008)

    Book  Google Scholar 

  4. Waller, B.N.: Classifying and analyzing analogies. Informal Logic 21(3), 199–218 (2001)

    Article  Google Scholar 

  5. Haraguchi, M., Arikawa, S.: Reasoning by Analogy as a Partial Identity between Models. Springer, Heidelberg (1987). pp. 61–87

    MATH  Google Scholar 

  6. Greiner, R.: Learning by Understanding Analogies. Springer, Boston (1986). pp. 81–84

    Book  Google Scholar 

  7. Winston, P.H.: Learning and reasoning by analogy. Commun. ACM 23(12), 689–703 (1980)

    Article  Google Scholar 

  8. Goebel, R.: A Sketch of Analogy as Reasoning with Equality Hypotheses. Springer, Heidelberg (1989). pp. 243–253

    Book  Google Scholar 

  9. Dung, P.M.: On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artif. Intell. 77(2), 321–358 (1995)

    Article  MathSciNet  Google Scholar 

  10. Racharak, T., Tojo, S., Hung, N.D., Boonkwan, P.: Argument-based logic programming for analogical reasoning. In: Proceedings of the 10th International Workshop on Juris-Informatics (JURISIN), pp. 73–86, November 2016

    Google Scholar 

  11. Racharak, T., Suntisrivaraporn, B., Tojo, S.: Identifying an Agent’s Preferences Toward Similarity Measures in Description Logics. Springer International Publishing, Cham (2016). pp. 201–208

    Book  Google Scholar 

  12. Racharak, T., Suntisrivaraporn, B., Tojo, S.: \(\sf sim^{\pi }\): a concept similarity measure under an agent’s preferences in description logic \(\cal{ELH}\). In: Proceedings of the 8th International Conference on Agents and Artificial Intelligence, pp. 480–487 (2016)

    Google Scholar 

  13. Nute, D.: Handbook of Logic in Artificial Intelligence and Logic Programming, vol. 3, pp. 353–395. Oxford University Press Inc, New York (1994)

    Google Scholar 

  14. Antoniou, G., Billington, D., Governatori, G., Maher, M.J.: Representation results for defeasible logic. ACM Trans. Comput. Logic 2(2), 255–287 (2001)

    Article  MathSciNet  Google Scholar 

  15. Amgoud, L., Cayrol, C.: On the acceptability of arguments in preference-based argumentation. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, UAI 1998, San Francisco, CA, USA, pp. 1–7. Morgan Kaufmann Publishers Inc. (1998)

    Google Scholar 

  16. Elvang-Gøransson, M., Krause, P.J., Fox, J.: Acceptability of Arguments as ‘Logical Uncertainty’. Springer, Heidelberg (1993). pp. 85–90

    Book  Google Scholar 

  17. García, A.J., Simari, G.R.: Defeasible logic programming: an argumentative approach. Theory Pract. Log. Program. 4(2), 95–138 (2004)

    Article  MathSciNet  Google Scholar 

  18. Prakken, H., Sartor, G.: Argument-based extended logic programming with defeasible priorities. J. Appl. Non-classical Logics 7, 25–75 (1997)

    Article  MathSciNet  Google Scholar 

  19. Damele, G.: Analogia Legis and Analogia Luris: An Overview from a Rhetorical Perspective. Springer International Publishing, Cham (2014). pp. 243–256

    Google Scholar 

  20. Ashley, K.: Case-Based Reasoning. Springer, Netherlands (2006). pp. 23–60

    Google Scholar 

  21. Aleven, V.: Teaching case-based argumentation through a model and examples (1997)

    Google Scholar 

  22. Prakken, H., Wyner, A.Z., Bench-Capon, T.J.M., Atkinson, K.: A formalization of argumentation schemes for legal case-based reasoning in ASPIC+. J. Log. Comput. 25(5), 1141–1166 (2015)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgments

This research is supported by JAIST, NECTEC, and SIIT under the dual doctoral degree program; and is partly supported by CILS of Thammasat University and the NRU project of Thailand Office of Higher Education Commission.

Author information

Authors and Affiliations

  1. School of Information, Computer, and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani, Thailand

    Teeradaj Racharak & Nguyen Duy Hung

  2. School of Information Science, Japan Advanced Institute of Science and Technology, Ishikawa, Japan

    Teeradaj Racharak & Satoshi Tojo

  3. National Electronics and Computer Technology Center, Pathum Thani, Thailand

    Prachya Boonkwan

Authors
  1. Teeradaj Racharak
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Satoshi Tojo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nguyen Duy Hung
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Prachya Boonkwan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Teeradaj Racharak .

Editor information

Editors and Affiliations

  1. Graduate School of Business Sciences, University of Tsukuba, Tokyo, Japan

    Setsuya Kurahashi

  2. Fujitsu Laboratories Ltd., Kanagawa, Japan

    Yuiko Ohta

  3. Chiba University, Chiba, Japan

    Sachiyo Arai

  4. National Institute of Informatics, Tokyo, Japan

    Ken Satoh

  5. Ochanomizu University, Tokyo, Japan

    Daisuke Bekki

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Racharak, T., Tojo, S., Hung, N.D., Boonkwan, P. (2017). Argument-Based Logic Programming for Analogical Reasoning. In: Kurahashi, S., Ohta, Y., Arai, S., Satoh, K., Bekki, D. (eds) New Frontiers in Artificial Intelligence. JSAI-isAI 2016. Lecture Notes in Computer Science(), vol 10247. Springer, Cham. https://doi.org/10.1007/978-3-319-61572-1_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-61572-1_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61571-4

  • Online ISBN: 978-3-319-61572-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation