Package org.tweetyproject.arg.rankings.reasoner

Class PropagationRankingReasoner

java.lang.Object

org.tweetyproject.arg.rankings.reasoner.AbstractRankingReasoner<LatticeArgumentRanking>

org.tweetyproject.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.

Note: This implementation only works for acyclic argument graphs. For cyclic graphs null is returned.

Author:

Anna Gessler

Nested Class Summary

Nested Classes

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

Constructor Summary

Constructors

Constructor	Description
PropagationRankingReasoner(boolean useMultiset)	Creates a new PropagationRankingReasoner with the given parameters.
PropagationRankingReasoner(double attackedArgumentsInfluence, boolean useMultiset, 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.
Collection<LatticeArgumentRanking>	getModels(DungTheory bbase)	Returns a characterizing model of the given belief base
boolean	isInstalled()	natively installed

Methods inherited from class java.lang.Object

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

Constructor Details

PropagationRankingReasoner

public PropagationRankingReasoner(boolean useMultiset)

Creates a new PropagationRankingReasoner with the given parameters.

Parameters:

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

PropagationRankingReasoner

public PropagationRankingReasoner(double attackedArgumentsInfluence,
 boolean useMultiset,
 PropagationRankingReasoner.PropagationSemantics semantics)

Creates a new PropagationRankingReasoner with the given parameters.

Parameters:

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

useMultiset - 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 Details

getModels

public 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.

isInstalled

public boolean isInstalled()

natively installed

Specified by:

isInstalled in class AbstractRankingReasoner<LatticeArgumentRanking>