net.sf.tweety.logics.ml

Class ApproximateNaiveMlnReasoner

java.lang.Object

net.sf.tweety.commons.Reasoner

net.sf.tweety.logics.ml.AbstractMlnReasoner

net.sf.tweety.logics.ml.ApproximateNaiveMlnReasoner

public class ApproximateNaiveMlnReasoner
extends AbstractMlnReasoner

This reasoner performs approximate reasoning with MLNs by considering only a subset of all Herbrand interpretations. This subset is chosen by first randomly selecting a set of Herbrand interpretations and then selecting the subset of this set with maximum weights.

Author:

Matthias Thimm

Constructor Summary

Constructors

Constructor and Description
ApproximateNaiveMlnReasoner(net.sf.tweety.commons.BeliefBase beliefBase, net.sf.tweety.logics.fol.syntax.FolSignature signature, long maxNumberOfSelectedInterpretations, long maxNumberOfInterpretationsForModel) Creates a new ApproximateNaiveMlnReasoner for the given Markov logic network.

Method Summary

Modifier and Type	Method and Description
protected double	doQuery(net.sf.tweety.logics.fol.syntax.FolFormula query) Performs the actual querying.
void	reset() Resets this reasoner (removes cached files etc.)

Methods inherited from class net.sf.tweety.logics.ml.AbstractMlnReasoner

computeWeight, getSignature, numberOfGroundSatisfactions, query

Methods inherited from class net.sf.tweety.commons.Reasoner

getKnowledgeBase

Methods inherited from class java.lang.Object

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

Constructor Detail

ApproximateNaiveMlnReasoner

public ApproximateNaiveMlnReasoner(net.sf.tweety.commons.BeliefBase beliefBase,
                                   net.sf.tweety.logics.fol.syntax.FolSignature signature,
                                   long maxNumberOfSelectedInterpretations,
                                   long maxNumberOfInterpretationsForModel)

Creates a new ApproximateNaiveMlnReasoner for the given Markov logic network.

Parameters:

beliefBase - a Markov logic network.

signature - another signature (if the probability distribution should be defined on that one (that one should subsume the signature of the Markov logic network)

maxNumberOfSelectedInterpretations - the maximum number of interpretations selected from the whole set of interpretations. Is -1 if all interpretations are to be selected.

maxNumberOfInterpretationsForModel - the maximum number of interpretations used for the model. Those interpretations are the subset of the interpretations selected with maximum weight. Is -1 if all interpretations are used for the model. It has to be maxNumberOfSelectedInterpretations >= maxNumberOfInterpretationsForModel.

Method Detail

reset

public void reset()

Description copied from class: AbstractMlnReasoner

Resets this reasoner (removes cached files etc.)

Specified by:

reset in class AbstractMlnReasoner

doQuery

protected double doQuery(net.sf.tweety.logics.fol.syntax.FolFormula query)

Description copied from class: AbstractMlnReasoner

Performs the actual querying.

Specified by:

doQuery in class AbstractMlnReasoner

Parameters:

query - a fol formula guaranteed to be ground.

Returns:

the answer of the query.