{"id":1057,"date":"2018-01-11T14:52:25","date_gmt":"2018-01-11T13:52:25","guid":{"rendered":"http:\/\/blogs.gm.fh-koeln.de\/ciop\/?page_id=1057"},"modified":"2024-11-20T12:46:08","modified_gmt":"2024-11-20T11:46:08","slug":"gbg-general-board-games","status":"publish","type":"page","link":"https:\/\/blogs.gm.fh-koeln.de\/ciop\/research\/gbg-general-board-games\/","title":{"rendered":"GBG: General Board Games"},"content":{"rendered":"

+++ Update 11\/2024: FutureWorkGBG<\/strong>: Project themes in GBG for students<\/a> (PDF or DOCX) +++<\/p>\n

\u00a0<\/span>Games are an interesting field in computer science concerning the question whether a computer can learn <\/strong><\/span>the game strategies just from self-play, without explicitly programming the tactics or performing exhaustive search. This is a branch of artificial intelligence (AI)<\/strong><\/span>. <\/span><\/p>\n

In January'2018 we released \"Gameboard<\/a>GBG<\/strong><\/span>, the G<\/strong><\/span>eneral<\/strong> B<\/strong><\/span>oard<\/strong> G<\/strong><\/span>ame<\/strong> playing and learning framework<\/strong> to the research community as another open-source<\/em> project.<\/span><\/span><\/p>\n

https:\/\/github.com\/WolfgangKonen\/GBG<\/a><\/p>\n

GBG takes the abstraction one level higher (than in the previous Connect-4 project<\/a>) in that it provides a software framework with standardized interfaces for arbitrary<\/em> games and arbitrary <\/em>AI agents. GBG helps students and researchers to take a quicker start-off into the area of game learning. Read more about GBG in the publications below<\/a>.<\/span><\/span><\/p>\n

Currently, the games implemented in GBG include 2048, Hex, Othello, Sim, Nim, Tic-Tac-Toe; more games are planned for the future. Current agents in GBG include MCTS, Max-N, Expectimax-N, TD-n-tuple and others.<\/p>\n

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, ...). The project is related to the research field known as General Game Playing (GGP)<\/strong><\/span>. The aim of GGP and GBG is it to develop agents which are able to learn a great variety of games. <\/span><\/p>\n

Read more about Games & Learning<\/a> ...<\/span><\/p>\n

FutureWorkGBG<\/strong>: Project themes in GBG for students<\/a> (PDF)<\/span><\/p>\n

 <\/p>\n

Contributors<\/h2>\n

Wolfgang Konen<\/a>, Markus Thill<\/a>, Samineh Bagheri<\/a>, Johannes Kutsch, Kevin Galitzki, Felix Barsnick, Yannick Dittmar, Julian C\u00f6ln, Johannes Scheiermann<\/p>\n

<\/a>Publications GBG<\/h2>\n

The article General Board Game Playing for Education and Research in Generic AI Game Learning (2019)<\/a> provides a scientific overview on GBG. The technical report The GBG Class Interface Tutorial V2.3: General Board Game Playing and Learning (Sep 2022)<\/a> gives an introduction to GBG more from the programmer's perspective.<\/p>\n

<\/a><\/form>

2023<\/h3>

Konen, Wolfgang<\/p>

Towards Learning Rubik's Cube with N-tuple-based Reinforcement Learning<\/a> Artikel<\/span> <\/p>

In: <\/span>arXiv preprint arXiv:2301.12167, <\/span>2023<\/span>.<\/p>

Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>

@article{Konen2023,
\r\ntitle = {Towards Learning Rubik's Cube with N-tuple-based Reinforcement Learning},
\r\nauthor = {Wolfgang Konen},
\r\nurl = {https:\/\/arxiv.org\/abs\/2301.12167},
\r\nyear  = {2023},
\r\ndate = {2023-01-01},
\r\nurldate = {2023-01-01},
\r\njournal = {arXiv preprint arXiv:2301.12167},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {article}
\r\n}
\r\n<\/pre><\/div>
Schlie\u00dfen<\/a><\/p><\/div>