Skip to main content
SpringerLink
Log in

Building user argumentative models

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Knowing how a user builds his/her arguments during a discussion gives useful advantages if we want to assist the user or analyse his/her argumentative skills. This paper presents a novel mechanism to build user argumentative models, which captures the argumentative style to generate arguments. To this end, we observe how users generate arguments, and apply a generalised association rules algorithm to discover rules for argument generation. These rules depict the argumentative style of the user. They are composed of an antecedent, which represents the conditions to build an argument, and a consequent, which represents such argument. To evaluate this proposal, we show results obtained in the domain of meeting scheduling. We discovered interesting rules from a group of users discussing in that domain, and checked that about 60% of the arguments that users had generated in a test situation can be also generated from the rules previously learnt, at least partially. Finally, although this work focuses on modelling users’ argumentative style, we discuss how this promising approach could be applied in different knowledge domains.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Ilgen DR, Hulin CL (2000) Computational modeling of behavior organizations: the third scientific discipline. American Psychological Association, Washington. Chap. 1

    Book  Google Scholar 

  2. Hedberg B (1981) How organizations learn and unlearn. In: Nystron PC, Starbuck WH (eds) Handbook of organizational design. Oxford University Press, New York, pp 3–27

    Google Scholar 

  3. Stein E, Zwass V (1995) Actualizing organizational memory with information systems. Inform Syst Res 6(2):85–117

    Article  Google Scholar 

  4. Srikant R, Agrawal R (1997) Mining generalized association rules. Future Gener Comput Syst 13(2–3):161–180

    Article  Google Scholar 

  5. Kraus S, Sycara K, Evenchik A (1998) Reaching agreements through argumentation: a logical model and implementation. Artif Intell 104(1–2):1–69

    Article  MATH  MathSciNet  Google Scholar 

  6. Brusilovsky P, Millán E (2007) User models for adaptive hypermedia and adaptive educational systems. In: The adaptive web. Springer, Berlin, pp 3–53

    Chapter  Google Scholar 

  7. Karlins M, Abelson HI (1970) Persuasion: how opinions and attitudes are changed. Springer, Berlin

    Google Scholar 

  8. O’Keefe D (1990) Persuasion: theory and research. SAGE, London

    Google Scholar 

  9. Sierra C, Jennings NR, Noriega P, Parsons S (1998) A framework for argumentation-based negotiation. In: Proceedings of the 4th international workshop on agent theories, architectures and languages, Rode Island, USA, pp 177–192

  10. Agrawal R, Imieliński T, Swami A (1993) Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD international conference on management of data, Washington, DC, pp 207–216

  11. Shintani T, Kitsuregawa M (1998) Parallel mining algorithms for generalized association rules with classification hierarchy. In Proceedings of the ACM SIGMOD international conference on management of data, SIGMOD 1998, Seattle, WA, pp 25–36

  12. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceedings of the 20th international conference very large data bases, Santiago de Chile, Chile, pp 487–499

  13. Rahwan I, Ramchurn SD, Jennings NR, McBurney P, Parsons S, Sonenberg L (2003) Argumentation-based negotiation. Knowl Eng Rev 18(4):343–375

    Article  Google Scholar 

  14. Maes P (1994) Agents that reduce work and information overload. Commun ACM 37(7):30–40

    Article  Google Scholar 

  15. Kautz H (1987) A formal theory of plan recognition. PhD thesis, Department of Computer Science, University of Rochester

  16. Charniak E, Goldman RP (1993) A Bayesian model of plan recognition. Artif Intell 64(1):53–79

    Article  Google Scholar 

  17. Ramchurn SD, Jennings R, Sierra C (2003) Persuasive negotiation for autonomous agents: a rhetorical approach. In: Proceedings of the IJCAIl workshop on computational models of natural argument, Acapulco, Mexico, pp 9–17

  18. Zukerman I, George S, George M (2003) Incorporating a user model into an information theoretic framework for argument interpretation. In: Proceedings of the ninth international conference on user modeling, Johnstown, PA, pp 106–116

Download references

Author information

Authors and Affiliations

  1. ISISTAN, Facultad de Ciencias Exactas, Universidad Nacional del Centro de la Pcia. Bs. As., Campus Universitario, Paraje Arroyo Seco, Tandil, Argentina

    Ariel Monteserin & Analía Amandi

  2. CONICET, Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires, Argentina

    Ariel Monteserin & Analía Amandi

Authors
  1. Ariel Monteserin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Analía Amandi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ariel Monteserin.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Monteserin, A., Amandi, A. Building user argumentative models. Appl Intell 32, 131–145 (2010). https://doi.org/10.1007/s10489-008-0139-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-008-0139-6

Keywords

Search

Navigation