Package net.sf.tweety.arg.rankings.reasoner

Class PropagationRankingReasoner

java.lang.Object

net.sf.tweety.arg.rankings.reasoner.AbstractRankingReasoner<LatticeArgumentRanking>

net.sf.tweety.arg.rankings.reasoner.PropagationRankingReasoner

All Implemented Interfaces:

ModelProvider<Argument,DungTheory,LatticeArgumentRanking>, PostulateEvaluatable<Argument>

public class PropagationRankingReasoner
extends AbstractRankingReasoner<LatticeArgumentRanking>

This class implements the argument ranking approach of [Delobelle. Ranking- based Semantics for Abstract Argumentation. Thesis, 2017] In this approach, initial values are assigned to arguments and then propagated into the graph. The paper describes three different ways of computing a ranking out of the propagation vector.

Author:

Anna Gessler

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static class	PropagationRankingReasoner.PropagationSemantics	The three propagation semantics: PROPAGATION1 ("Propa_epsilon") PROPAGATION2 ("Propa_{1+epsilon}") PROPAGATION3 ("Propa_{1->epsilon}")

Constructor Summary

Constructors

Constructor	Description
PropagationRankingReasoner(boolean use_multiset)	Creates a new PropagationRankingReasoner with the given parameters.
PropagationRankingReasoner(double attacked_arguments_influence, boolean use_multiset, PropagationRankingReasoner.PropagationSemantics semantics)	Creates a new PropagationRankingReasoner with the given parameters.

Method Summary

Modifier and Type	Method	Description
LatticeArgumentRanking	getModel(DungTheory kb)	Returns a single (dedicated) model of the given belief base.
java.util.Collection<LatticeArgumentRanking>	getModels(DungTheory bbase)	Returns a characterizing model of the given belief base

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

PropagationRankingReasoner

public PropagationRankingReasoner(boolean use_multiset)

Creates a new PropagationRankingReasoner with the given parameters.

Parameters:

use_multiset - determines whether the multiset (M) of attackers/defenders of length is used instead of the set (S)

PropagationRankingReasoner

public PropagationRankingReasoner(double attacked_arguments_influence,
                                  boolean use_multiset,
                                  PropagationRankingReasoner.PropagationSemantics semantics)

Creates a new PropagationRankingReasoner with the given parameters.

Parameters:

attacked_arguments_influence - the smaller this value is, the more important is the influence of the non-attacked arguments.

use_multiset - determines whether the multiset (M) of attackers/defenders of length is used instead of the set (S)

semantics - one of the three propagation semantics

Method Detail

getModels

public java.util.Collection<LatticeArgumentRanking> getModels(DungTheory 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 LatticeArgumentRanking getModel(DungTheory 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.