Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | AIIDE 2020 Workshops |
| Untertitel | Joint Proceedings of the AIIDE 2020 Workshops co-located with 16th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE 2020) |
| Publikationsstatus | Veröffentlicht - 2020 |
| Extern publiziert | Ja |
| Veranstaltung | 2020 Joint Artificial Intelligence and Interactive Digital Entertainment Workshops, AIIDE-WS 2020 - Worcester, USA / Vereinigte Staaten Dauer: 19 Okt. 2020 → … |
Publikationsreihe
| Name | CEUR Workshop Proceedings |
|---|---|
| Herausgeber (Verlag) | CEUR Workshop Proceedings |
| Band | 2862 |
| ISSN (Print) | 1613-0073 |
Abstract
Strategy games are complex environments often used in AI-research to evaluate new algorithms. Despite the commonalities of most strategy games, often research is focused on one game only, which may lead to bias or overfitting to a particular environment. In this paper, we motivate and present STRATEGA - a general strategy games framework for playing n-player turn-based and real-time strategy games. The platform currently implements turn-based games, which can be configured via YAML-files. It exposes an API with access to a forward model to facilitate research on statistical forward planning agents. The framework and agents can log information during games for analysing and debugging algorithms. We also present some sample rule-based agents, as well as search-based agents like Monte Carlo Tree Search and Rolling Horizon Evolution, and quantitatively analyse their performance to demonstrate the use of the framework. Results, although purely illustrative, show the known problems that traditional search-based agents have when dealing with high branching factors in these games.
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AIIDE 2020 Workshops: Joint Proceedings of the AIIDE 2020 Workshops co-located with 16th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE 2020). 2020. (CEUR Workshop Proceedings; Band 2862).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - STRATEGA - A general strategy games framework
AU - Dockhorn, Alexander
AU - Hurtado-Grueso, Jorge
AU - Jeurissen, Dominik
AU - Perez-Liebana, Diego
N1 - Funding Information: This work is supported by UK EPSRC research grant EP/T008962/1.
PY - 2020
Y1 - 2020
N2 - Strategy games are complex environments often used in AI-research to evaluate new algorithms. Despite the commonalities of most strategy games, often research is focused on one game only, which may lead to bias or overfitting to a particular environment. In this paper, we motivate and present STRATEGA - a general strategy games framework for playing n-player turn-based and real-time strategy games. The platform currently implements turn-based games, which can be configured via YAML-files. It exposes an API with access to a forward model to facilitate research on statistical forward planning agents. The framework and agents can log information during games for analysing and debugging algorithms. We also present some sample rule-based agents, as well as search-based agents like Monte Carlo Tree Search and Rolling Horizon Evolution, and quantitatively analyse their performance to demonstrate the use of the framework. Results, although purely illustrative, show the known problems that traditional search-based agents have when dealing with high branching factors in these games.
AB - Strategy games are complex environments often used in AI-research to evaluate new algorithms. Despite the commonalities of most strategy games, often research is focused on one game only, which may lead to bias or overfitting to a particular environment. In this paper, we motivate and present STRATEGA - a general strategy games framework for playing n-player turn-based and real-time strategy games. The platform currently implements turn-based games, which can be configured via YAML-files. It exposes an API with access to a forward model to facilitate research on statistical forward planning agents. The framework and agents can log information during games for analysing and debugging algorithms. We also present some sample rule-based agents, as well as search-based agents like Monte Carlo Tree Search and Rolling Horizon Evolution, and quantitatively analyse their performance to demonstrate the use of the framework. Results, although purely illustrative, show the known problems that traditional search-based agents have when dealing with high branching factors in these games.
UR - http://www.scopus.com/inward/record.url?scp=85106177098&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2009.05643
DO - 10.48550/arXiv.2009.05643
M3 - Conference contribution
AN - SCOPUS:85106177098
T3 - CEUR Workshop Proceedings
BT - AIIDE 2020 Workshops
T2 - 2020 Joint Artificial Intelligence and Interactive Digital Entertainment Workshops, AIIDE-WS 2020
Y2 - 19 October 2020
ER -