An Approach to Argumentation Considering Attacks through Time

  • Maximiliano C. D. Budán
  • Mauro Gómez Lucero
  • Carlos I. Chesñevar
  • Guillermo R. Simari
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7520)

Abstract

In the last decade, several argument-based formalisms have emerged, with application in many areas, such as legal reasoning, autonomous agents and multi-agent systems; many are based on Dung’s seminal work characterizing Abstract Argumentation Frameworks (AF). Recent research in the area has led to Temporal Argumentation Frameworks (TAF), that extend AF by considering the temporal availability of arguments. A new framework was introduced in subsequent research, called Extended Temporal Argumentation Framework (E-TAF), extending TAF with the capability of modeling the availability of attacks among arguments. E-TAF is powerful enough to model different time-dependent properties associated with arguments; moreover, we will present an instantiation of the abstract framework E-TAF on an extension of Defeasible Logic Programming (DeLP) incorporating the representation of temporal availability and strength factors of arguments varying over time, associating these characteristics with the language of DeLP. The strength factors are used to model different more concrete measures such as reliability, priorities, etc.; the information is propagated to the level of arguments, then the E-TAF definitions are applied establishing their temporal acceptability.

Keywords

Argumentation Temporal Argumentation Defeasible Logic Programming Argument and Computation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Amgoud, L., Devred, C.: Argumentation Frameworks as Constraint Satisfaction Problems. In: Benferhat, S., Grant, J. (eds.) SUM 2011. LNCS, vol. 6929, pp. 110–122. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  2. 2.
    Amgoud, L., Prade, H.: Using arguments for making and explaining decisions. Artificial Intelligence 173(3-4), 413–436 (2009)MathSciNetMATHCrossRefGoogle Scholar
  3. 3.
    Augusto, J.C., Simari, G.R.: Temporal defeasible reasoning. Knowledge and Information Systems 3(3), 287–318 (2001)MATHCrossRefGoogle Scholar
  4. 4.
    Baroni, P., Giacomin, M.: Semantics of abstract argument systems. In: Rahwan, I., Simari, G.R. (eds.) Argumentation in Artificial Intelligence, pp. 24–44. Springer (2009)Google Scholar
  5. 5.
    Beierle, C., Freund, B., Kern-Isberner, G., Thimm, M.: Using defeasible logic programming for argumentation-based decision support in private law. In: COMMA. Frontiers in Artificial Intelligence and Applications, vol. 216, pp. 87–98. IOS Press (2010)Google Scholar
  6. 6.
    Besnard, P., Hunter, A.: Elements of Argumentation. MIT Press (2008)Google Scholar
  7. 7.
    Brena, R., Chesñevar, C.I.: Information distribution decisions supported by argumentation. In: Encyclopedia of Decision Making and Decision Support Technologies, pp. 309–315. Information Science Reference, USA (2008)Google Scholar
  8. 8.
    Brewka, G., Dunne, P.E., Woltran, S.: Relating the semantics of abstract dialectical frameworks and standard AFs. In: Walsh, T. (ed.) IJCAI, pp. 780–785. IJCAI/AAAI (2011)Google Scholar
  9. 9.
    Budán, M.C.D., Lucero, M.G., Chesñevar, C.I., Simari, G.R.: Modeling time and reliability in structured argumentation frameworks. In: KR 2012, pp. 578–582 (2012)Google Scholar
  10. 10.
    Cobo, M.L., Martínez, D.C., Simari, G.R.: On admissibility in timed abstract argumentation frameworks. In: ECAI. Frontiers in Artificial Intelligence and Applications, vol. 215, pp. 1007–1008. IOS Press (2010)Google Scholar
  11. 11.
    Cobo, M.L., Martinez, D.C., Simari, G.R.: Acceptability in Timed Frameworks with Intermittent Arguments. In: Iliadis, L., Maglogiannis, I., Papadopoulos, H. (eds.) Artificial Intelligence Applications and Innovations, Part II. IFIP AICT, vol. 364, pp. 202–211. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  12. 12.
    Dung, P.M.: On the acceptability of arguments and its fundamental role in nonmonotonic reasoning and logic programming and n-person games. Artificial Intelligence 77, 321–357 (1995)MathSciNetMATHCrossRefGoogle Scholar
  13. 13.
    Galitsky, B., McKenna, E.W.: Sentiment extraction from consumer reviews for providing product recommendations. Patent Application, US 2009/0282019 A1 (November 2009)Google Scholar
  14. 14.
    García, A.J., Simari, G.R.: Defeasible logic programming: An argumentative approach. Theory Practice of Logic Programming 4(1), 95–138 (2004)MATHCrossRefGoogle Scholar
  15. 15.
    Mann, N., Hunter, A.: Argumentation using temporal knowledge. In: COMMA 2008, pp. 204–215 (2008)Google Scholar
  16. 16.
    Modgil, S., Caminada, M.: Proof theories and algorithms for abstract argumentation frameworks. In: Rahwan, I., Simari, G.R. (eds.) Argumentation in Artificial Intelligence, pp. 105–132. Springer (2009)Google Scholar
  17. 17.
    Pardo, P., Godo, L.: t-DeLP: A Temporal Extension of the Defeasible Logic Programming Argumentative Framework. In: Benferhat, S., Grant, J. (eds.) SUM 2011. LNCS, vol. 6929, pp. 489–503. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  18. 18.
    Pasquier, P., Hollands, R., Rahwan, I., Dignum, F., Sonenberg, L.: An empirical study of interest-based negotiation. Autonomous Agents and Multi-Agent Systems 22(2), 249–288 (2011)CrossRefGoogle Scholar
  19. 19.
    Prakken, H.: An abstract framework for argumentation with structured arguments. Argument and Computation 1, 93–124 (2010)CrossRefGoogle Scholar
  20. 20.
    Rahwan, I., Ramchurn, S.D., Jennings, N.R., Mcburney, P., Parsons, S., Sonenberg, L.: Argumentation-based negotiation. Knowl. Eng. Rev. 18, 343–375 (2003)CrossRefGoogle Scholar
  21. 21.
    Rahwan, I., Simari, G.R.: Argumentation in Artificial Intelligence. Springer (2009)Google Scholar
  22. 22.
    van der Weide, T.L., Dignum, F., Meyer, J.-J.C., Prakken, H., Vreeswijk, G.A.W.: Multi-criteria argument selection in persuasion dialogues. In: AAMAS 2011, Richland, SC, vol. 3, pp. 921–928 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Maximiliano C. D. Budán
    • 1
    • 2
    • 3
  • Mauro Gómez Lucero
    • 1
    • 2
  • Carlos I. Chesñevar
    • 1
    • 2
  • Guillermo R. Simari
    • 2
  1. 1.Argentine National Council of Scientific and Technical Research (CONICET)Argentina
  2. 2.AI R&D Lab (LIDIA)Universidad Nacional del Sur in Bahía BlancaArgentina
  3. 3.Universidad Nacional de Santiago del EsteroArgentina

Personalised recommendations