Package org.tweetyproject.arg.rankings.reasoner

Class CountingRankingReasoner

java.lang.Object
org.tweetyproject.arg.rankings.reasoner.AbstractRankingReasoner<NumericalArgumentRanking>
org.tweetyproject.arg.rankings.reasoner.CountingRankingReasoner
All Implemented Interfaces:
ModelProvider<Argument,​ArgumentationFramework,​NumericalArgumentRanking>, PostulateEvaluatable<Argument>
public class CountingRankingReasoner extends AbstractRankingReasoner<NumericalArgumentRanking>
This class implements the argument ranking approach of [Pu, Zhang, G.Luo, J.Luo. Attacker and Defender Counting Approach for Abstract Argumentation. CoRR 2015]. This approach ranks arguments by counting the number of their attackers and defenders in form of a dialogue game where proponents are defenders and opponents are attackers.
Author:
Anna Gessler

  • Constructor Details

    • CountingRankingReasoner

      public CountingRankingReasoner()
      Create a new CountingRankingReasoner with default parameters.

    • CountingRankingReasoner

      public CountingRankingReasoner​(double damping_factor, double epsilon)
      Create a new CountingRankingReasoner with the given parameters.
      Parameters:
      damping_factor - must be in (0,1)
      epsilon - TODO add description

    • CountingRankingReasoner

      public CountingRankingReasoner​(double damping_factor)
      Create a new CountingRankingReasoner with the given parameters.
      Parameters:
      damping_factor - must be in (0,1)

  • Method Details

    • getModels

      public Collection<NumericalArgumentRanking> getModels​(ArgumentationFramework bbase)
      Description copied from interface: ModelProvider
      Returns a characterizing model of the given belief base
      Parameters:
      bbase - some belief base
      Returns:
      the (selected) models of the belief base

    • getModel

      public NumericalArgumentRanking getModel​(ArgumentationFramework kb)
      Description copied from interface: ModelProvider
      Returns a single (dedicated) model of the given belief base. If the implemented method allows for more than one dedicated model, the selection may be non-deterministic.
      Parameters:
      kb - some belief base
      Returns:
      a selected model of the belief base.