Solving Weighted Argumentation Frameworks with Soft Constraints

  • Stefano Bistarelli
  • Daniele Pirolandi
  • Francesco Santini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6384)


We suggest soft constraints as a mean to parametrically represent and solve “weighted” Argumentation problems: different kinds of preference levels related to arguments, e.g. a score representing a “fuzziness”, a “cost” or a probability level of each argument, can be represented by choosing different semiring algebraic structures. The novel idea is to provide a common computational and quantitative framework where the computation of the classical Dung’s extensions, e.g. the admissible extension, has an associated score representing “how much good” the set is. Preference values associated to arguments are clearly more informative and can be used to prefer a given set of arguments over others with the same characteristics (e.g. admissibility). Moreover, we propose a mapping from weighted Argumentation Frameworks to Soft Constraint Satisfaction Problems (SCSPs); with this mapping we can compute Dung semantics (e.g. admissible and stable) by solving the related SCSP. To implement this mapping we use JaCoP, a Java constraint solver.


Multiagent System Soft Constraint Argumentation Framework Argument System Complete Extension 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Stefano Bistarelli
    • 1
    • 2
  • Daniele Pirolandi
    • 1
  • Francesco Santini
    • 1
    • 3
  1. 1.Dipartimento di Matematica e InformaticaUniversità di PerugiaItaly
  2. 2.Istituto di Informatica e Telematica (CNR)PisaItaly
  3. 3.Dipartimento di ScienzeUniversità “G. d’Annunzio”PescaraItaly

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