Games & Learning | CIOP

+++ Update 10/2020: FutureWorkGBG: Project themes in GBG for students (PDF) +++

(see also: GBG: General Board Games in CIOP )

Games are interesting in computer science concerning the question whether a computer can learn the game strategies just from self-play, without explicitly programming the tactics or performing exhaustive search. This is a branch of artificial intelligence (AI). Many game learning approaches are based on reinforcement learning, namely TD-learning.

In our research group we have studied extensively the game Connect-4 ("Four-in-a-Row"). We were able to develop an agent which learns Connect-4 nearly perfectly just from self-play. Our Java-based Connect-4 Game Playing Framework (C4GPF) is open-source for interested researchers. Read more...

In January'2018 we released GBG, the General Board Game playing and learning framework to the research community as another open-source project.

https://github.com/WolfgangKonen/GBG

Read more about GBG ...

The long-term goal of our research group is it to transfer these learning strategies to many other games (dots-and-boxes, go, Poker, checkers, Abalone, Sim, Othello, ...). The project is related to the research field known as General Game Playing (GGP). The aim of GGP is it to develop agents which are able to learn a great variety of games.

People

Wolfgang Konen, Markus Thill, Samineh Bagheri, Johannes Kutsch, Kevin Galitzki, Felix Barsnick, Julian Cöln, Yannick Dittmar, Johannes Scheiermann

Publications

2023

Konen, Wolfgang

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

In: arXiv preprint arXiv:2301.12167, 2023.

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).

2022

Konen, Wolfgang

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

TH Köln 2022.

Weitz, Ann

Untersuchung von selbstlernenden Reinforcement Learning Agenten im computergenerierten Spiel Yavalath Abschlussarbeit

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

Weitz, Ann

Entwicklung einer allgemeinen Schnittstelle zwischen Ludii und dem GBG Framework Forschungsbericht

2022, (Praxisprojekt).

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).

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).

Scheiermann, Johannes; Konen, Wolfgang

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

In: IEEE Transactions on Games, 2022.

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).

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).

Konen, Wolfgang; Bagheri, Samineh

Final adaptation reinforcement learning for N-player games Artikel

In: arXiv preprint arXiv:2111.14375, 2021.

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).

Scheiermann, Johannes

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

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

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.

Konen, Wolfgang; Bagheri, Samineh

Final Adaptation Reinforcement Learning for N-Player Games Forschungsbericht

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

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.

2019

Cöln, Julian; Dittmar, Yannick

Untersuchung von KI Agenten im Spiel Othello Forschungsbericht

TH Köln, Institut für Informatik 2019.

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.

Barsnick, Felix

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

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

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).

Kutsch, Johannes

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

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

2015

Konen, Wolfgang

Reinforcement Learning for Board Games: The Temporal Difference Algorithm Forschungsbericht

Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Sciences, 2015.

Bagheri, Samineh; Thill, Markus; Koch, Patrick; Konen, Wolfgang

Online Adaptable Learning Rates for the Game Connect-4 Artikel

In: IEEE Transactions on Computational Intelligence and AI in Games, Bd. (accepted 11/2014), S. 1, 2015.

Konen, Wolfgang

Reinforcement Learning für Brettspiele: Der Temporal Difference Algorithmus Forschungsbericht

Research Center CIOP (Computational Intelligence, Optimization and Data Mining) Cologne University of Applied Sciences, 2015, (Updated version 2015).

2014

Bagheri, Samineh; Thill, Markus; Koch, Patrick; Konen, Wolfgang

Online Adaptable Learning Rates for the Game Connect-4 Forschungsbericht

CIplus Nr. TR 03/2014, 2014, (Preprint version of the article in IEEE Transactions on Computational Intelligence and AI in Games, 2015).

Konen, Wolfgang; Koch, Patrick

Adaptation in Nonlinear Learning Models for Nonstationary Tasks Proceedings Article

In: Filipic, Bogdan (Hrsg.): PPSN'2014: 13th International Conference on Parallel Problem Solving From Nature, Ljubljana, Springer, Heidelberg, 2014.

Thill, Markus; Bagheri, Samineh; Koch, Patrick; Konen, Wolfgang

Temporal Difference Learning with Eligibility Traces for the Game Connect-4 Proceedings Article

In: Preuss, Mike; Rudolph, Günther (Hrsg.): CIG'2014, International Conference on Computational Intelligence in Games, Dortmund, 2014.

Thill, Markus; Konen, Wolfgang

Connect-4 Game Playing Framework (C4GPF) Sonstige

2014.

2012

Thill, Markus

Reinforcement Learning mit N-Tupel-Systemen für Vier Gewinnt Abschlussarbeit

TH Köln – University of Applied Sciences, 2012, (Bachelor thesis, 1st prize in Opitz award 2013, Festo award 2012, Ferchau award 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.

2009

Konen, Wolfgang; Bartz-Beielstein, Thomas

Reinforcement learning for games: failures and successes -- CMA-ES and TDL in comparision Proceedings Article

In: GECCO '09: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference, S. 2641–2648, ACM, Montreal, Québec, Canada, 2009.

2008

Konen, Wolfgang

Reinforcement Learning für Brettspiele: Der Temporal Difference Algorithmus Forschungsbericht

Cologne University of Applied Sciences 2008.

Konen, Wolfgang; Bartz-Beielstein, Thomas

Reinforcement Learning für strategische Brettspiele Forschungsbericht

Cologne University of Applied Sciences 2008.

Related Posts:

Presenting our Approach to Explainable Reinforcement…

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