Publications | CIOP

2023

Konen, Wolfgang

Towards Learning Rubik's Cube with N-tuple-based Reinforcement Learning Artikel

In: arXiv preprint arXiv:2301.12167, 2023.

Links | BibTeX | Schlagwörter: CI, Game Learning, GBG, general game playing, learning, optimization, Reinforcement learning

Seven, Meltem

KI-Agenten im Vergleich: Erfolg von Agenten des Ludii General Game Systems in Partien gegen perfekte oder starke Agenten am Beispiel der Spiele Vier Gewinnt, Nim und Othello Abschlussarbeit

TH Köln – University of Applied Sciences, 2023, (Bachelor thesis).

Links | BibTeX | Schlagwörter: BT-MT, Game Learning, GBG, Ludii

2022

Konen, Wolfgang

The GBG Class Interface Tutorial V2.3: General Board Game Playing and Learning Forschungsbericht

TH Köln 2022.

Links | BibTeX | Schlagwörter: CI, Game Learning, GBG, general game playing, learning, optimization, Reinforcement learning

Cöln, Julian

KI-Konzepte für das Erlernen nicht-deterministischer Spiele am Beispiel von "EinStein würfelt nicht!" Abschlussarbeit

TH Köln – University of Applied Sciences, 2022, (Bachelor thesis).

Links | BibTeX | Schlagwörter: AI, BT-MT, Game Learning, GBG, Reinforcement learning

Scheiermann, Johannes; Konen, Wolfgang

AlphaZero-Inspired Game Learning: Faster Training by Using MCTS Only at Test Time Artikel

In: arXiv preprint arXiv:2204.13307, 2022, (Preprint of the IEEE ToG 2022 paper).

Links | BibTeX | Schlagwörter: CI, Game Learning, GBG, general game playing, learning, optimization, Reinforcement learning

Scheiermann, Johannes; Konen, Wolfgang

AlphaZero-Inspired Game Learning: Faster Training by Using MCTS Only at Test Time Artikel

In: IEEE Transactions on Games, 2022.

Links | BibTeX | Schlagwörter: AI, deep learning, Game Learning, GBG, learning, optimization, Reinforcement learning

2021

Meissner, Simon

Untersuchung des Spiel- und Lernerfolgs künstlicher Intelligenzen für ein nichtdeterministisches Spiel mit imperfekten Informationen: Blackjack in der Game-Learning-Umgebung ’General Board Game’ (GBG) Abschlussarbeit

TH Köln – University of Applied Sciences, 2021, (Bachelor thesis).

Links | BibTeX | Schlagwörter: AI, BT-MT, Game Learning, GBG, machine learning

Zeh, Tim

Untersuchung von allgemeinen KI-Agenten für das Spiel Poker im General Board Games Framework Abschlussarbeit

TH Köln – University of Applied Sciences, 2021, (Master thesis).

Links | BibTeX | Schlagwörter: AI, BT-MT, Game Learning, GBG, machine learning

Konen, Wolfgang; Bagheri, Samineh

Final adaptation reinforcement learning for N-player games Artikel

In: arXiv preprint arXiv:2111.14375, 2021.

Links | BibTeX | Schlagwörter: CI, Game Learning, GBG, general game playing, learning, optimization, Reinforcement learning

2020

Scheiermann, Johannes

Sind (trainierte) General-Purpose-RL-Agenten im Brettspiel Othello stärker als (untrainierte) General-Game-Playing Agenten? Forschungsbericht

TH Köln, Institut für Informatik 2020, (Praxisprojekt).

Links | BibTeX | Schlagwörter: AI, BT-MT, Game Learning, GBG, machine learning, Reinforcement learning

Scheiermann, Johannes

AlphaZero-inspirierte KI-Agenten im General Board Game Playing Abschlussarbeit

TH Köln -- University of Applied Sciences, 2020, (Bachelor thesis).

Links | BibTeX | Schlagwörter: AI, BT-MT, Game Learning, GBG, machine learning, Reinforcement learning

Konen, Wolfgang; Bagheri, Samineh

Reinforcement Learning for N-Player Games: The Importance of Final Adaptation Proceedings Article

In: Vasile, Bogdan Filipic Massimiliano (Hrsg.): 9th International Conference on Bioinspired Optimisation Methods and Their Applications (BIOMA), Bruxelles, 2020.

Links | BibTeX | Schlagwörter: CI, Game Learning, GBG, general game playing, learning, optimization, Reinforcement learning

Konen, Wolfgang; Bagheri, Samineh

Final Adaptation Reinforcement Learning for N-Player Games Forschungsbericht

Research Center CIOP (Computational Intelligence, Optimization and Data Mining) 2020.

Links | BibTeX | Schlagwörter: CI, Game Learning, GBG, general game playing, learning, optimization, Reinforcement learning

Konen, Wolfgang

The GBG Class Interface Tutorial V2.2: General Board Game Playing and Learning Forschungsbericht

Research Center CIOP (Computational Intelligence, Optimization and Data Mining) 2020.

Links | BibTeX | Schlagwörter: CI, Game Learning, GBG, general game playing, learning, optimization, Reinforcement learning

2019

Cöln, Julian; Dittmar, Yannick

Untersuchung von KI Agenten im Spiel Othello Forschungsbericht

TH Köln, Institut für Informatik 2019.

Links | BibTeX | Schlagwörter: AI, BT-MT, Game Learning, GBG, machine learning, Reinforcement learning

Konen, Wolfgang

General Board Game Playing for Education and Research in Generic AI Game Learning Proceedings Article

In: Perez, Diego; Mostaghim, Sanaz; Lucas, Simon (Hrsg.): IEEE Conference on Games, London, 2019.

Links | BibTeX | Schlagwörter: CI, Game Learning, GBG, general game playing, learning, optimization, Reinforcement learning

Barsnick, Felix

Implementierung und Untersuchung eines Turniersystems für KI-Agenten in Brettspielen Abschlussarbeit

TH Köln -- University of Applied Sciences, 2019, (Master thesis).

Links | BibTeX | Schlagwörter: BT-MT, Elo, Game Learning, GBG, Glicko, machine learning, Reinforcement learning

2017

Galitzki, Kevin

Selbstlernende Agenten für das skalierbare Spiel Hex: Untersuchung verschiedener KI-Verfahren im GBG-Framework Abschlussarbeit

TH Köln -- University of Applied Sciences, 2017, (Bachelor thesis).

Links | BibTeX | Schlagwörter: BT-MT, CI, Game Learning, GBG, learning, optimization, Reinforcement learning

Kutsch, Johannes

KI-Agenten fur das Spiel 2048: Untersuchung von Lernalgorithmen für nichtdeterministische Spiele Abschlussarbeit

TH Köln -- University of Applied Sciences, 2017, (Bachelor thesis).

Links | BibTeX | Schlagwörter: BT-MT, CI, Game Learning, GBG, learning, optimization, Reinforcement learning

2012

Thill, Markus; Koch, Patrick; Konen, Wolfgang

Reinforcement learning with n-tuples on the game Connect-4 Proceedings Article

In: Coello, Carlos A. Coello; Cutello, Vincenzo (Hrsg.): PPSN'2012: 12th International Conference on Parallel Problem Solving From Nature, Taormina, S. 184–194, Springer, Heidelberg, 2012.

Links | BibTeX | Schlagwörter: Game Learning, GBG, SOMA

Search Field

20 Einträge « ‹ 1 von 2 › »

1.

Konen, Wolfgang

Towards Learning Rubik's Cube with N-tuple-based Reinforcement Learning Artikel

In: arXiv preprint arXiv:2301.12167, 2023.

2.

Seven, Meltem

KI-Agenten im Vergleich: Erfolg von Agenten des Ludii General Game Systems in Partien gegen perfekte oder starke Agenten am Beispiel der Spiele Vier Gewinnt, Nim und Othello Abschlussarbeit

TH Köln – University of Applied Sciences, 2023, (Bachelor thesis).

3.

Konen, Wolfgang

The GBG Class Interface Tutorial V2.3: General Board Game Playing and Learning Forschungsbericht

TH Köln 2022.

4.

Cöln, Julian

KI-Konzepte für das Erlernen nicht-deterministischer Spiele am Beispiel von "EinStein würfelt nicht!" Abschlussarbeit

TH Köln – University of Applied Sciences, 2022, (Bachelor thesis).

5.

Scheiermann, Johannes; Konen, Wolfgang

AlphaZero-Inspired Game Learning: Faster Training by Using MCTS Only at Test Time Artikel

In: arXiv preprint arXiv:2204.13307, 2022, (Preprint of the IEEE ToG 2022 paper).

6.

Scheiermann, Johannes; Konen, Wolfgang

AlphaZero-Inspired Game Learning: Faster Training by Using MCTS Only at Test Time Artikel

In: IEEE Transactions on Games, 2022.

7.

Meissner, Simon

Untersuchung des Spiel- und Lernerfolgs künstlicher Intelligenzen für ein nichtdeterministisches Spiel mit imperfekten Informationen: Blackjack in der Game-Learning-Umgebung ’General Board Game’ (GBG) Abschlussarbeit

TH Köln – University of Applied Sciences, 2021, (Bachelor thesis).

8.

Zeh, Tim

Untersuchung von allgemeinen KI-Agenten für das Spiel Poker im General Board Games Framework Abschlussarbeit

TH Köln – University of Applied Sciences, 2021, (Master thesis).

9.

Konen, Wolfgang; Bagheri, Samineh

Final adaptation reinforcement learning for N-player games Artikel

In: arXiv preprint arXiv:2111.14375, 2021.

10.

Scheiermann, Johannes

Sind (trainierte) General-Purpose-RL-Agenten im Brettspiel Othello stärker als (untrainierte) General-Game-Playing Agenten? Forschungsbericht

TH Köln, Institut für Informatik 2020, (Praxisprojekt).

20 Einträge « ‹ 1 von 2 › »

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