• Title of article

    Dynamics of argumentation systems: A division-based method Original Research Article

  • Author/Authors

    Beishui Liao، نويسنده , , Li Jin، نويسنده , , Robert C. Koons، نويسنده ,

  • Issue Information
    روزنامه با شماره پیاپی سال 2011
  • Pages
    25
  • From page
    1790
  • To page
    1814
  • Abstract
    The changing of arguments and their attack relation is an intrinsic property of a variety of argumentation systems. So, it is very important to efficiently figure out how the status of arguments in a system evolves when the system is updated. However, unlike other areas of argumentation that have been deeply explored, such as argumentation semantics, proof theories, and algorithms, etc., dynamics of argumentation systems has been comparatively neglected. In this paper, we formulate a general theory (called a division-based method) to cope with this problem based on a new concept: the division of an argumentation framework. When an argumentation framework is updated, it is divided into three parts: an unaffected, an affected, and a conditioning part. The status of arguments in the unaffected sub-framework remains unchanged, while the status of the affected arguments is computed in a special argumentation framework (called a conditioned argumentation framework, or briefly CAF) that is composed of an affected part and a conditioning part. We have proved that under a certain semantics that satisfies the directionality criterion (complete, preferred, ideal, or grounded semantics), the extensions of the updated framework are equal to the result of a combination of the extensions of an unaffected sub-framework and sets of the extensions of a set of assigned CAFs. Due to the efficiency of the division-based method, it is expected to be very useful in various kinds of argumentation systems where arguments and attacks are dynamics.
  • Keywords
    Abstract argumentation frameworks , Computational complexity , Semantics of argumentation , Dynamics of argumentation
  • Journal title
    Artificial Intelligence
  • Serial Year
    2011
  • Journal title
    Artificial Intelligence
  • Record number

    1207871