ImprovedRiveretTheoryLearner

java.lang.Object
- net.sf.tweety.arg.dung.learning.ImprovedRiveretTheoryLearner

```
public class ImprovedRiveretTheoryLearner
extends java.lang.Object
```
Implementation of the algorithm for learning (grounded) labelings from with some improvements: Riveret, Régis, and Guido Governatori. "On learning attacks in probabilistic abstract argumentation." 2016. - instead of stopping when no undecided attacks are left, we always use the full budget. While slower, greatly improves results. - added additional parameter for pruning instead of pruning at 0. approx. -10% of budget gives better results. eg budget: 1000 threshold: -100 - added fifth rule to capture self-attacking arguments

Author:

Lars Bengel

Constructor Summary

Constructors
Constructor	Description
`ImprovedRiveretTheoryLearner(java.util.Collection<Argument> arguments, int max_cycles)`	initialize learner for the given set of arguments

Method Summary

Modifier and Type	Method	Description
`DungTheory`	`learnLabelings(java.util.ArrayList<Labeling> labelings)`	learn theory without pruning discarded attacks
`DungTheory`	`learnLabelings(java.util.ArrayList<Labeling> labelings, boolean prune, int threshold)`	learn random labelings from the given List until no undecided attacks are left in the theory

Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Constructor Detail
  - ImprovedRiveretTheoryLearner
```
public ImprovedRiveretTheoryLearner(java.util.Collection<Argument> arguments,
                                    int max_cycles)
```
    initialize learner for the given set of arguments
    
    Parameters:
    
    arguments - a set of arguments
    
    max_cycles - TODO description needed
- Method Detail
  - learnLabelings
```
public DungTheory learnLabelings(java.util.ArrayList<Labeling> labelings,
                                 boolean prune,
                                 int threshold)
```
    learn random labelings from the given List until no undecided attacks are left in the theory
    
    Parameters:
    
    labelings - a list of labelings
    
    prune - if true, remove discarded attacks after each step
    
    threshold - TODO description needed
    
    Returns:
    
    the learned dung theory
  - learnLabelings
```
public DungTheory learnLabelings(java.util.ArrayList<Labeling> labelings)
```
    learn theory without pruning discarded attacks
    
    Parameters:
    
    labelings - a list of labelings
    
    Returns:
    
    the learned dung theory