Deliberative Argumentation for Smart Environments

  • Juan Carlos Nieves
  • Esteban Guerrero
  • Jayalakshmi Baskar
  • Helena Lindgren
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8861)

Abstract

In this paper, an argumentation-based deliberative approach for fusing contextual information obtained from heterogeneous sources using a multi-agent system is introduced. The system is characterized by three different agents: an Environment Agent, an Activity Agent and a Coach Agent. These agents consider data from heterogenous sources of data. As a method for aggregating data and supporting decision-making, so-called agreement rules are instrumental in the argumentation-based deliberative method. The aggregation rules will be associated to specific beliefs related to the services of each agent.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Amgoud, L., Prade, H.: Reaching agreement through argumentation: A possibilistic approach. In: Principles of Knowledge Representation and Reasoning: Proceedings of the Ninth International Conference (KR 2004), Whistler, Canada, June 2-5, pp. 175–182. AAAI Press (2004)Google Scholar
  2. 2.
    Atkinson, K., Bench-Capon, T., Walton, D.: Distinctive features of persuasion and deliberation dialogues. Argument and Computation 4(2), 105–127 (2012)CrossRefGoogle Scholar
  3. 3.
    Black, E., Hunter, A.: An inquiry dialogue system. Autonomous Agents and Multi-Agent Systems 19(2), 173–209 (2009)CrossRefGoogle Scholar
  4. 4.
    Gelder, A.V., Ross, K.A., Schlipf, J.S.: The well-founded semantics for general logic programs. Journal of the ACM 38(3), 620–650 (1991)MATHGoogle Scholar
  5. 5.
    Gelfond, M., Lifschitz, V.: Classical negation in logic programs and disjunctive databases. New Generation Computing 9(3-4), 365–385 (1991)CrossRefGoogle Scholar
  6. 6.
    Guerrero, E., Nieves, J.C., Lindgren, H.: Arguing through the Well-founded Semantics: an Argumentation Engine. Submitted to a Journal (2014)Google Scholar
  7. 7.
    Kaptelinin, V.: Activity theory: Implications for human-computer interaction. In: Nardi, B. (ed.) Context and Consciousness. Activity Theory and Human Computer Interaction, pp. 103–116. MIT Press (1996)Google Scholar
  8. 8.
    Kraus, S., Sycara, K.P., Evenchik, A.: Reaching agreements through argumentation: A logical model and implementation. Artif. Intell. 104(1-2), 1–69 (1998)MathSciNetCrossRefMATHGoogle Scholar
  9. 9.
    Lindgren, H., Surie, D., Nilsson, I.: Agent-supported assessment for adaptive and personalized ambient assisted living. In: Corchado, J.M., Pérez, J.B., Hallenborg, K., Golinska, P., Corchuelo, R. (eds.) Trends in Practical Applications of Agents and Multiagent Systems. AISC, vol. 90, pp. 25–32. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  10. 10.
    Marcais, J., Spanoudakis, N., Moraitis, P.: Using argumentation for ambient assisted living. In: Iliadis, L., Maglogiannis, I., Papadopoulos, H. (eds.) EANN/AIAI 2011, Part II. IFIP AICT, vol. 364, pp. 410–419. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  11. 11.
    Sá, S., Alcântara, J.: Cooperative dialogues with conditional arguments. In: Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems, vol. 1, pp. 501–508. International Foundation for Autonomous Agents and Multiagent Systems (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Juan Carlos Nieves
    • 1
  • Esteban Guerrero
    • 1
  • Jayalakshmi Baskar
    • 1
  • Helena Lindgren
    • 1
  1. 1.Department of Computing ScienceUmeå UniversityUmeåSweden

Personalised recommendations