c4board

+++ 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 Gameboard Hex in GBGGBG, 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

2022

Konen, Wolfgang

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

TH Köln 2022.

Links | BibTeX

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

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

2021

Konen, Wolfgang; Bagheri, Samineh

Final adaptation reinforcement learning for N-player games Artikel

In: arXiv preprint arXiv:2111.14375, 2021.

Links | BibTeX

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

Scheiermann, Johannes

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

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

Links | BibTeX

Konen, Wolfgang; Bagheri, Samineh

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

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

Links | BibTeX

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

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

2019

Cöln, Julian; Dittmar, Yannick

Untersuchung von KI Agenten im Spiel Othello Forschungsbericht

TH Köln, Institut für Informatik 2019.

Links | BibTeX

Konen, Wolfgang

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

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

Links | BibTeX

Barsnick, Felix

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

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

Links | BibTeX

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

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

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.

Links | BibTeX

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.

Links | BibTeX

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).

Links | BibTeX

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).

Links | BibTeX

Konen, Wolfgang; Koch, Patrick

Adaptation in Nonlinear Learning Models for Nonstationary Tasks Konferenzbeitrag

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

Links | BibTeX

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

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

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

Links | BibTeX

Thill, Markus; Konen, Wolfgang

Connect-4 Game Playing Framework (C4GPF) Sonstige

2014.

Links | BibTeX

2009

Konen, Wolfgang; Bartz-Beielstein, Thomas

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

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

Links | BibTeX

2008

Konen, Wolfgang

Reinforcement Learning für Brettspiele: Der Temporal Difference Algorithmus Forschungsbericht

Cologne University of Applied Sciences 2008.

Links | BibTeX

Konen, Wolfgang; Bartz-Beielstein, Thomas

Reinforcement Learning für strategische Brettspiele Forschungsbericht

Cologne University of Applied Sciences 2008.

Links | BibTeX