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This artifact provides the implementation of the L* learning algorithm described in the paper "Learning Regular
Sets from Queries and Counterexamples" (https://doi.org/10.1016/0890-5401(87)90052-6) by Dana Angluin including
variations and optimizations thereof such as the versions based on "On the Learnability of Infinitary Regular
Sets" (https://dx.doi.org/10.1006/inco.1995.1070) by Oded Maler and Amir Pnueli or "Inference of finite automata
using homing sequences" (http://dx.doi.org/10.1006/inco.1993.1021) by Ronald L. Rivest and Robert E. Schapire.
/* Copyright (C) 2013-2018 TU Dortmund
* This file is part of LearnLib, http://www.learnlib.de/.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package de.learnlib.algorithms.lstar;
import java.util.List;
import de.learnlib.algorithms.lstar.AbstractAutomatonLStar.StateInfo;
import de.learnlib.datastructure.observationtable.GenericObservationTable;
/**
* Class that contains all data that represent the internal state of the {@link AbstractAutomatonLStar} learner and its
* DFA and Mealy implementations.
*
* @param
* The input alphabet type.
* @param
* The output domain type.
* @param
* The hypothesis type.
* @param
* The hypothesis state type.
*
* @author bainczyk
*/
public class AutomatonLStarState extends AbstractLStarState {
private final AI hypothesis;
private final List> stateInfos;
AutomatonLStarState(final GenericObservationTable observationTable,
final AI hypothesis,
final List> stateInfos) {
super(observationTable);
this.hypothesis = hypothesis;
this.stateInfos = stateInfos;
}
AI getHypothesis() {
return hypothesis;
}
List> getStateInfos() {
return stateInfos;
}
}