Multilevel Aggregation of Arguments in a Model Driven Approach to Assess an Argumentation Relevance

  • Olivier Poitou
  • Claire Saurel
Part of the Communications in Computer and Information Science book series (CCIS, volume 443)


Figuring out which hypothesis best explain an observed ongoing situation can be a critical issue. This paper introduces a generic model based approach to support users during this task. It then focuses on an hypothesis relevance scoring function that helps users to efficently build a convincing argumentation towards hypothesis. This function uses a multi-level extension of Yager’s aggregation algorithm, exploiting both the strength of the components of an argumentation, and the confidence the user puts in them. The presented work was illustrated on a maritime surveillance application.


decision support argumentation model-based approach multilevel multicriteria aggregation 


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  1. 1.
    Poitou, O., Saurel, C.: Supporting situation assessment by threat modeling and belief analysis. In: OCOSS Ocean and Coastal Observation: Sensors and observing Systems, Numerical Models and Information. Nice (2013)Google Scholar
  2. 2.
    Choquet, G.: Theory of capacities. In: Annales de l’Institut Fourier – tome 5, pp. 131–295 (1953)Google Scholar
  3. 3.
    Yager, R.: Prioritized aggregation operators. International Journal of Approximate Reasoning 48, 263–274 (2008)CrossRefzbMATHMathSciNetGoogle Scholar
  4. 4.
    da Costa Pereira, C., Dragoni, M., Pasi, G.: A prioritized “and” aggregation operator for multidimensional relevance assessment. In: Serra, R., Cucchiara, R. (eds.) AI*IA 2009. LNCS (LNAI), vol. 5883, pp. 72–81. Springer, Heidelberg (2009)Google Scholar
  5. 5.
    Grabisch, M.: Fuzzy Measures and Integrals: Theory and Applications. Springer-Verlag New York, Inc., Secaucus (2000)Google Scholar
  6. 6.
    Dung, P.: On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artificial Intelligence 77, 321–357 (1995)CrossRefzbMATHMathSciNetGoogle Scholar
  7. 7.
    Bench-Capon, T.: Value-based argumentation frameworks, pp. 7–22. Taylor & Francis (2002)Google Scholar
  8. 8.
    Pollock, J.: Defeasible reasoning and degrees of justification. In: Argument and Computation, vol. 1(1), pp. 7–22. Taylor & Francis (2010)Google Scholar
  9. 9.
    Maximiliano, C.D., Budan, M.J., Gomez Lucero, G.R.S.: Modeling reliability varying over time through a labeled argumentative framework. In: IJCAI 2013 workshop on weighted logics for Artificial Intelligence, WL4AI 2013 (2013)Google Scholar
  10. 10.
    Demolombe, R.: Graded trust. AAMAS Trust, pp. 1–12 (2009)Google Scholar
  11. 11.
    Ray, C., Granger, A., Thibaud, R., Etienne, L.: Temporal rule-based analysis of maritime traffic. In: OCOSS Ocean and Coastal Observation: Sensors and Observing Systems, Numerical Models and Information, pp. 171–178. Nice (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Olivier Poitou
    • 1
  • Claire Saurel
    • 1
  1. 1.ONERAToulouseFrance

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