Details
| Original language | English |
|---|---|
| Title of host publication | 2021 IEEE Conference on Games, CoG 2021 |
| Publisher | IEEE Computer Society |
| ISBN (electronic) | 9781665438865 |
| Publication status | Published - 2021 |
| Externally published | Yes |
| Event | 2021 IEEE Conference on Games, CoG 2021 - Copenhagen, Denmark Duration: 17 Aug 2021 → 20 Aug 2021 |
Publication series
| Name | IEEE Conference on Computatonal Intelligence and Games, CIG |
|---|---|
| Volume | 2021-August |
| ISSN (Print) | 2325-4270 |
| ISSN (electronic) | 2325-4289 |
Abstract
Designing agents that are able to achieve different play-styles while maintaining a competitive level of play is a difficult task, especially for games for which the research community has not found super-human performance yet, like strategy games. These require the AI to deal with large action spaces, long-term planning and partial observability, among other well-known factors that make decision-making a hard problem. On top of this, achieving distinct play-styles using a general algorithm without reducing playing strength is not trivial. In this paper, we propose Portfolio Monte Carlo Tree Search with Progressive Unpruning for playing a turn-based strategy game (Tribes) and show how it can be parameterized so a quality-diversity algorithm (MAP-Elites) is used to achieve different play-styles while keeping a competitive level of play. Our results show that this algorithm is capable of achieving these goals even for an extensive collection of game levels beyond those used for training.
ASJC Scopus subject areas
- Computer Science(all)
- Artificial Intelligence
- Computer Science(all)
- Computer Graphics and Computer-Aided Design
- Computer Science(all)
- Computer Vision and Pattern Recognition
- Computer Science(all)
- Human-Computer Interaction
- Computer Science(all)
- Software
Cite this
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2021 IEEE Conference on Games, CoG 2021. IEEE Computer Society, 2021. (IEEE Conference on Computatonal Intelligence and Games, CIG; Vol. 2021-August).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Generating Diverse and Competitive Play-Styles for Strategy Games
AU - Perez-Liebana, Diego
AU - Guerrero-Romero, Cristina
AU - Dockhorn, Alexander
AU - Xu, Linjie
AU - Hurtado, Jorge
AU - Jeurissen, Dominik
N1 - Funding Information: Work supported by UK EPSRC grants EP/T008962/1, EP/L015846/1 and Queen Mary’s Apocrita HPC facility.
PY - 2021
Y1 - 2021
N2 - Designing agents that are able to achieve different play-styles while maintaining a competitive level of play is a difficult task, especially for games for which the research community has not found super-human performance yet, like strategy games. These require the AI to deal with large action spaces, long-term planning and partial observability, among other well-known factors that make decision-making a hard problem. On top of this, achieving distinct play-styles using a general algorithm without reducing playing strength is not trivial. In this paper, we propose Portfolio Monte Carlo Tree Search with Progressive Unpruning for playing a turn-based strategy game (Tribes) and show how it can be parameterized so a quality-diversity algorithm (MAP-Elites) is used to achieve different play-styles while keeping a competitive level of play. Our results show that this algorithm is capable of achieving these goals even for an extensive collection of game levels beyond those used for training.
AB - Designing agents that are able to achieve different play-styles while maintaining a competitive level of play is a difficult task, especially for games for which the research community has not found super-human performance yet, like strategy games. These require the AI to deal with large action spaces, long-term planning and partial observability, among other well-known factors that make decision-making a hard problem. On top of this, achieving distinct play-styles using a general algorithm without reducing playing strength is not trivial. In this paper, we propose Portfolio Monte Carlo Tree Search with Progressive Unpruning for playing a turn-based strategy game (Tribes) and show how it can be parameterized so a quality-diversity algorithm (MAP-Elites) is used to achieve different play-styles while keeping a competitive level of play. Our results show that this algorithm is capable of achieving these goals even for an extensive collection of game levels beyond those used for training.
UR - http://www.scopus.com/inward/record.url?scp=85122950146&partnerID=8YFLogxK
U2 - 10.1109/CoG52621.2021.9619094
DO - 10.1109/CoG52621.2021.9619094
M3 - Conference contribution
AN - SCOPUS:85122950146
T3 - IEEE Conference on Computatonal Intelligence and Games, CIG
BT - 2021 IEEE Conference on Games, CoG 2021
PB - IEEE Computer Society
T2 - 2021 IEEE Conference on Games, CoG 2021
Y2 - 17 August 2021 through 20 August 2021
ER -