Package org.tweetyproject.arg.dung.learning

Class TheoryLearner

java.lang.Object

org.tweetyproject.arg.dung.learning.TheoryLearner

public class TheoryLearner
extends java.lang.Object

Improved version of the MaxSAT algorithm from: Niskanen, Andreas, Johannes Wallner, and Matti Järvisalo. "Synthesizing argumentation frameworks from examples." Journal of Artificial Intelligence Research 66 (2019) This algorithm supports 4-valued labelings as proposed in Riveret, Régis, and Guido Governatori. "On learning attacks in probabilistic abstract argumentation." 2016.

Author:

Lars Bengel

Constructor Summary

Constructors

Constructor	Description
TheoryLearner(java.util.Collection<Argument> args, Semantics semantics, java.lang.String solverLocation)

Method Summary

Modifier and Type	Method	Description
DungTheory	learnExamples(java.util.Map<Labeling,java.lang.Integer> positiveExamples, java.util.Map<Labeling,java.lang.Integer> negativeExamples)	learns an argumentation framework fulfilling as many positive examples as possible while trying to not fulfill any negative examples

Methods inherited from class java.lang.Object

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

Constructor Detail

TheoryLearner

public TheoryLearner(java.util.Collection<Argument> args,
                     Semantics semantics,
                     java.lang.String solverLocation)
              throws java.lang.NoSuchMethodException

Parameters:

args - a set of arguments

semantics - a semantics

solverLocation - path to the open wbo solver binary

Throws:

java.lang.NoSuchMethodException - if the semantics is not implemented

Method Detail

learnExamples

public DungTheory learnExamples(java.util.Map<Labeling,java.lang.Integer> positiveExamples,
                                java.util.Map<Labeling,java.lang.Integer> negativeExamples)
                         throws java.io.IOException,
                                java.lang.reflect.InvocationTargetException,
                                java.lang.IllegalAccessException

learns an argumentation framework fulfilling as many positive examples as possible while trying to not fulfill any negative examples

Parameters:

positiveExamples - a map of positive examples and their weights

negativeExamples - a map of negative examples and their weights

Returns:

a argumentation framework inferred from the given examples

Throws:

java.io.IOException - if an error occurs

java.lang.reflect.InvocationTargetException - if an error occurs

java.lang.IllegalAccessException - if an error occurs