Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019 |
| Editors | Vilem Novak, Vladimir Marik, Martin Stepnicka, Mirko Navara, Petr Hurtik |
| Publisher | Atlantis Press SARL |
| Pages | 536-543 |
| Number of pages | 8 |
| ISBN (electronic) | 9789462527706 |
| Publication status | Published - 2020 |
| Externally published | Yes |
| Event | 11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019 - Prague, Czech Republic Duration: 9 Sept 2019 → 13 Sept 2019 |
Publication series
| Name | Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019 |
|---|
Abstract
Developing agents for automated game playing is a demanding task in the general game production cycle. Especially the involvement of frequent balance changes after the release, which for example often occur in collectible card games, require constant updates of the developed agent. The game's developers need to continuously analyze and understand the current meta-game for adjusting the agent's parameters, making balance changes to the game, and, thereby, sustaining the satisfaction of its player base. The underlying analysis largely depends on evaluating players' play traces. Necessary adjustments to the agent's and the game's parameters are taken care of by the game's developers. This paper proposes a first step in automatically observing the current state of a collectible card game, which will assist the developers in their understanding of established deck archetypes and, therefore, speed up the update cycle. Fuzzy multisets are used for modeling decks and frequently occurring subsets of cards. We propose the definition of a (fuzzy) multiset centroid to uniquely represent the cluster and its contained decks and show that it is better able to match the deck archetype than the often reported deck core. The proposed clustering procedure identifies deck archetypes and keeps track of its common variants in the current meta-game. We evaluate the approach by comparing the result of our clustering procedure with a hand-labeled data set and show that it is able to reproduce clusters of similar quality to a labeling provided by experts.
Keywords
- Clustering, Fuzzy multisets, Hearthstone, Meta-game analysis
ASJC Scopus subject areas
- Computer Science(all)
- Computational Theory and Mathematics
- Computer Science(all)
- Information Systems
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Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019. ed. / Vilem Novak; Vladimir Marik; Martin Stepnicka; Mirko Navara; Petr Hurtik. Atlantis Press SARL, 2020. p. 536-543 (Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Fuzzy multiset clustering for metagame analysis
AU - Dockhorn, Alexander
AU - Schwensfeier, Tony
AU - Kruse, Rudolf
PY - 2020
Y1 - 2020
N2 - Developing agents for automated game playing is a demanding task in the general game production cycle. Especially the involvement of frequent balance changes after the release, which for example often occur in collectible card games, require constant updates of the developed agent. The game's developers need to continuously analyze and understand the current meta-game for adjusting the agent's parameters, making balance changes to the game, and, thereby, sustaining the satisfaction of its player base. The underlying analysis largely depends on evaluating players' play traces. Necessary adjustments to the agent's and the game's parameters are taken care of by the game's developers. This paper proposes a first step in automatically observing the current state of a collectible card game, which will assist the developers in their understanding of established deck archetypes and, therefore, speed up the update cycle. Fuzzy multisets are used for modeling decks and frequently occurring subsets of cards. We propose the definition of a (fuzzy) multiset centroid to uniquely represent the cluster and its contained decks and show that it is better able to match the deck archetype than the often reported deck core. The proposed clustering procedure identifies deck archetypes and keeps track of its common variants in the current meta-game. We evaluate the approach by comparing the result of our clustering procedure with a hand-labeled data set and show that it is able to reproduce clusters of similar quality to a labeling provided by experts.
AB - Developing agents for automated game playing is a demanding task in the general game production cycle. Especially the involvement of frequent balance changes after the release, which for example often occur in collectible card games, require constant updates of the developed agent. The game's developers need to continuously analyze and understand the current meta-game for adjusting the agent's parameters, making balance changes to the game, and, thereby, sustaining the satisfaction of its player base. The underlying analysis largely depends on evaluating players' play traces. Necessary adjustments to the agent's and the game's parameters are taken care of by the game's developers. This paper proposes a first step in automatically observing the current state of a collectible card game, which will assist the developers in their understanding of established deck archetypes and, therefore, speed up the update cycle. Fuzzy multisets are used for modeling decks and frequently occurring subsets of cards. We propose the definition of a (fuzzy) multiset centroid to uniquely represent the cluster and its contained decks and show that it is better able to match the deck archetype than the often reported deck core. The proposed clustering procedure identifies deck archetypes and keeps track of its common variants in the current meta-game. We evaluate the approach by comparing the result of our clustering procedure with a hand-labeled data set and show that it is able to reproduce clusters of similar quality to a labeling provided by experts.
KW - Clustering
KW - Fuzzy multisets
KW - Hearthstone
KW - Meta-game analysis
UR - http://www.scopus.com/inward/record.url?scp=85084237464&partnerID=8YFLogxK
U2 - 10.2991/eusflat-19.2019.74
DO - 10.2991/eusflat-19.2019.74
M3 - Conference contribution
AN - SCOPUS:85084237464
T3 - Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019
SP - 536
EP - 543
BT - Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019
A2 - Novak, Vilem
A2 - Marik, Vladimir
A2 - Stepnicka, Martin
A2 - Navara, Mirko
A2 - Hurtik, Petr
PB - Atlantis Press SARL
T2 - 11th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2019
Y2 - 9 September 2019 through 13 September 2019
ER -