Player Identification for Collectible Card Games with Dynamic Game States
Collectible card games are a fruitful test space for studying resource allocation and battle strategy, given that their structures promote reactionary combat styles and allow players to obtain variable amounts of combat power by expending fixed resources. However, their large action spaces also allo...
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| Format: | Article |
| Language: | English |
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LibraryPress@UF
2023-05-01
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| Series: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
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| Online Access: | https://journals.flvc.org/FLAIRS/article/view/133244 |
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| _version_ | 1849736565431992320 |
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| author | Logan Fields John Licato |
| author_facet | Logan Fields John Licato |
| author_sort | Logan Fields |
| collection | DOAJ |
| description | Collectible card games are a fruitful test space for studying resource allocation and battle strategy, given that their structures promote reactionary combat styles and allow players to obtain variable amounts of combat power by expending fixed resources. However, their large action spaces also allow for flexibility in play styles, thus facilitating behavioral analysis at the individual level rather than the aggregate level. When presented with the same options and the same amount of resources, a player's selection of cards and their choice of moves gives insight into their unique play style and decision-making tendencies. As such, we use the virtual collectible card game Legends of Code and Magic to determine whether we can identify a player from their actions and, conversely, predict the future actions of a known player. Our main contributions to this task are the creation of a comprehensive dataset of Legends of Code and Magic game states and actions, as well as the first use of large transformer-based language models to address this problem. |
| format | Article |
| id | doaj-art-cf3abb11a8704f28889d3ac3e5659d44 |
| institution | DOAJ |
| issn | 2334-0754 2334-0762 |
| language | English |
| publishDate | 2023-05-01 |
| publisher | LibraryPress@UF |
| record_format | Article |
| series | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| spelling | doaj-art-cf3abb11a8704f28889d3ac3e5659d442025-08-20T03:07:14ZengLibraryPress@UFProceedings of the International Florida Artificial Intelligence Research Society Conference2334-07542334-07622023-05-013610.32473/flairs.36.13324469550Player Identification for Collectible Card Games with Dynamic Game StatesLogan Fields0https://orcid.org/0000-0002-9069-8355John Licato1https://orcid.org/0000-0003-4700-9750University of South FloridaUniversity of South FloridaCollectible card games are a fruitful test space for studying resource allocation and battle strategy, given that their structures promote reactionary combat styles and allow players to obtain variable amounts of combat power by expending fixed resources. However, their large action spaces also allow for flexibility in play styles, thus facilitating behavioral analysis at the individual level rather than the aggregate level. When presented with the same options and the same amount of resources, a player's selection of cards and their choice of moves gives insight into their unique play style and decision-making tendencies. As such, we use the virtual collectible card game Legends of Code and Magic to determine whether we can identify a player from their actions and, conversely, predict the future actions of a known player. Our main contributions to this task are the creation of a comprehensive dataset of Legends of Code and Magic game states and actions, as well as the first use of large transformer-based language models to address this problem.https://journals.flvc.org/FLAIRS/article/view/133244collectible card gamesplayer modelingnatural language processing |
| spellingShingle | Logan Fields John Licato Player Identification for Collectible Card Games with Dynamic Game States Proceedings of the International Florida Artificial Intelligence Research Society Conference collectible card games player modeling natural language processing |
| title | Player Identification for Collectible Card Games with Dynamic Game States |
| title_full | Player Identification for Collectible Card Games with Dynamic Game States |
| title_fullStr | Player Identification for Collectible Card Games with Dynamic Game States |
| title_full_unstemmed | Player Identification for Collectible Card Games with Dynamic Game States |
| title_short | Player Identification for Collectible Card Games with Dynamic Game States |
| title_sort | player identification for collectible card games with dynamic game states |
| topic | collectible card games player modeling natural language processing |
| url | https://journals.flvc.org/FLAIRS/article/view/133244 |
| work_keys_str_mv | AT loganfields playeridentificationforcollectiblecardgameswithdynamicgamestates AT johnlicato playeridentificationforcollectiblecardgameswithdynamicgamestates |