Package org.tweetyproject.arg.dung.reasoner

Class StronglyAdmissibleReasoner

java.lang.Object

org.tweetyproject.arg.dung.reasoner.AbstractDungReasoner

org.tweetyproject.arg.dung.reasoner.AbstractExtensionReasoner

org.tweetyproject.arg.dung.reasoner.StronglyAdmissibleReasoner

All Implemented Interfaces:

ModelProvider<Argument,DungTheory,Extension<DungTheory>>, PostulateEvaluatable<Argument>, QualitativeReasoner<DungTheory,Argument>, Reasoner<Boolean,DungTheory,Argument>

public class StronglyAdmissibleReasoner
extends AbstractExtensionReasoner

Reasoner for strong admissibility

A set of arguments E is strongly admissible iff all every argument 'a' in E is defended by some argument 'b' in E \ {a}, which itself is strongly defended by E \ {a}, i.e. no argument in E is defended only by itself

Author:

Lars Bengel

Constructor Summary

Constructors

Constructor	Description
StronglyAdmissibleReasoner()

Method Summary

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

Methods inherited from class org.tweetyproject.arg.dung.reasoner.AbstractExtensionReasoner

getSimpleReasonerForSemantics, isInstalled, query, query

Methods inherited from class java.lang.Object

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

Constructor Details

StronglyAdmissibleReasoner

public StronglyAdmissibleReasoner()

Method Details

getModels

public Collection<Extension<DungTheory>> 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 Extension<DungTheory> getModel(DungTheory bbase)

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:

bbase - some belief base

Returns:

a selected model of the belief base.