Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2024 IEEE Conference on Games, CoG 2024 |
| Publisher | IEEE Computer Society |
| ISBN (electronic) | 9798350350678 |
| ISBN (print) | 979-8-3503-5068-5 |
| Publication status | Published - 28 Aug 2024 |
| Event | 6th Annual IEEE Conference on Games, CoG 2024 - Milan, Italy Duration: 5 Aug 2024 → 8 Aug 2024 |
Publication series
| Name | IEEE Conference on Computatonal Intelligence and Games, CIG |
|---|---|
| ISSN (Print) | 2325-4270 |
| ISSN (electronic) | 2325-4289 |
Abstract
Playing strategy games is a challenging problem for artificial intelligence (AI). One of the major challenges is the large search space due to a diverse set of game components. In recent works, state abstraction has been applied to search-based game AI and has brought significant performance improvements. State abstraction techniques rely on reducing the search space, e.g., by aggregating similar states. However, the application of these abstractions is hindered because the quality of an abstraction is difficult to evaluate. Previous works hence abandon the abstraction in the middle of the search to not bias the search to a local optimum. This mechanism introduces a hyper-parameter to decide the time to abandon the current state abstraction. In this work, we propose a size-constrained state abstraction (SCSA), an approach that limits the maximum number of nodes being grouped together. We found that with SCSA, the abstraction is not required to be abandoned. Our empirical results on 3 strategy games show that the SCSA agent outperforms the previous methods and yields robust performance over different games. Codes are opensourced at https://anonymous.4open.science/r/SCSA-DB44/.
Keywords
- Game artificial intelligence, monte carlo tree search, planning, state abstraction
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
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Proceedings of the 2024 IEEE Conference on Games, CoG 2024. IEEE Computer Society, 2024. (IEEE Conference on Computatonal Intelligence and Games, CIG).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Strategy Game-Playing with Size-Constrained State Abstraction
AU - Xu, Linjie
AU - Perez-Liebana, Diego
AU - Dockhorn, Alexander
N1 - Publisher Copyright: © 2024 IEEE.
PY - 2024/8/28
Y1 - 2024/8/28
N2 - Playing strategy games is a challenging problem for artificial intelligence (AI). One of the major challenges is the large search space due to a diverse set of game components. In recent works, state abstraction has been applied to search-based game AI and has brought significant performance improvements. State abstraction techniques rely on reducing the search space, e.g., by aggregating similar states. However, the application of these abstractions is hindered because the quality of an abstraction is difficult to evaluate. Previous works hence abandon the abstraction in the middle of the search to not bias the search to a local optimum. This mechanism introduces a hyper-parameter to decide the time to abandon the current state abstraction. In this work, we propose a size-constrained state abstraction (SCSA), an approach that limits the maximum number of nodes being grouped together. We found that with SCSA, the abstraction is not required to be abandoned. Our empirical results on 3 strategy games show that the SCSA agent outperforms the previous methods and yields robust performance over different games. Codes are opensourced at https://anonymous.4open.science/r/SCSA-DB44/.
AB - Playing strategy games is a challenging problem for artificial intelligence (AI). One of the major challenges is the large search space due to a diverse set of game components. In recent works, state abstraction has been applied to search-based game AI and has brought significant performance improvements. State abstraction techniques rely on reducing the search space, e.g., by aggregating similar states. However, the application of these abstractions is hindered because the quality of an abstraction is difficult to evaluate. Previous works hence abandon the abstraction in the middle of the search to not bias the search to a local optimum. This mechanism introduces a hyper-parameter to decide the time to abandon the current state abstraction. In this work, we propose a size-constrained state abstraction (SCSA), an approach that limits the maximum number of nodes being grouped together. We found that with SCSA, the abstraction is not required to be abandoned. Our empirical results on 3 strategy games show that the SCSA agent outperforms the previous methods and yields robust performance over different games. Codes are opensourced at https://anonymous.4open.science/r/SCSA-DB44/.
KW - Game artificial intelligence
KW - monte carlo tree search
KW - planning
KW - state abstraction
UR - http://www.scopus.com/inward/record.url?scp=85203538562&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2408.06202
DO - 10.48550/arXiv.2408.06202
M3 - Conference contribution
AN - SCOPUS:85203538562
SN - 979-8-3503-5068-5
T3 - IEEE Conference on Computatonal Intelligence and Games, CIG
BT - Proceedings of the 2024 IEEE Conference on Games, CoG 2024
PB - IEEE Computer Society
T2 - 6th Annual IEEE Conference on Games, CoG 2024
Y2 - 5 August 2024 through 8 August 2024
ER -