BurdenBasedRankingReasoner

java.lang.Object
- net.sf.tweety.arg.rankings.reasoner.AbstractRankingReasoner<LatticeArgumentRanking>
- - net.sf.tweety.arg.rankings.reasoner.BurdenBasedRankingReasoner

All Implemented Interfaces:

ModelProvider<Argument,DungTheory,LatticeArgumentRanking>, PostulateEvaluatable<Argument>
```
public class BurdenBasedRankingReasoner
extends AbstractRankingReasoner<LatticeArgumentRanking>
```
This class implements the "Burden-based" argument semantics approach as proposed by [Amgoud, Ben-Naim. Ranking-based semantics for argumentation frameworks. 2013]. It assigns a "Burden number" to every argument.

Author:

Anna Gessler

Nested Class Summary

Nested Classes
Modifier and Type Class Description

class BurdenBasedRankingReasoner.LexicographicTupleComparator
Compares burden numbers according to the lexicographic ordering.

Constructor Summary

Constructors
Constructor Description

BurdenBasedRankingReasoner()

Method Summary

Modifier and Type	Method	Description
`LatticeArgumentRanking`	`getModel(DungTheory base)`	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
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Constructor Detail
  - BurdenBasedRankingReasoner
```
public BurdenBasedRankingReasoner()
```
- 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 base)
```
    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:
    
    base - some belief base
    
    Returns:
    
    a selected model of the belief base.