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This artifact provides the implementation of the TTT algorithm as described in the paper "The TTT Algorithm: A Redundancy-Free Approach to Active Automata Learning" (https://doi.org/10.1007/978-3-319-11164-3_26) by Malte Isberner, Falk Howar, and Bernhard Steffen.

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/* Copyright (C) 2013-2023 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.algorithm.ttt.moore;

import de.learnlib.algorithm.ttt.base.TTTState;
import de.learnlib.algorithm.ttt.base.TTTTransition;
import net.automatalib.automaton.transducer.MooreMachine;
import net.automatalib.word.Word;

/**
 * A {@link MooreMachine}-specific state of the {@link TTTHypothesisMoore} class.
 *
 * @param 
 *         input symbol type
 * @param 
 *         output symbols type
 */
public class TTTStateMoore extends TTTState> {

    O output;

    public TTTStateMoore(int initialAlphabetSize, TTTTransition> parentTransition, int id) {
        super(initialAlphabetSize, parentTransition, id);
    }

    public O getOutput() {
        return output;
    }

    public void setOutput(O output) {
        this.output = output;
    }
}