Details
| Original language | English |
|---|---|
| Pages (from-to) | 1207-1217 |
| Number of pages | 11 |
| Journal | International Journal of Computational Intelligence Systems |
| Volume | 13 |
| Issue number | 1 |
| Early online date | 18 Aug 2020 |
| Publication status | Published - 2020 |
| Externally published | Yes |
Abstract
Search-based agents have shown to perform well in many game-based applications. In the context of partially-observable sce-narios agent’s require the state to be fully determinized. Especially in case of collectible cards games, the sheer number of decks constructed by players hinder an agent to reliably estimate the game’s current state, and therefore, renders the search ineffective. In this paper, we propose the use of a (fuzzy) multiset representation to describe frequently played decks. Extracted deck prototypes have shown to match human expert labels well and seem to serve as an efficient abstraction of the deck space. We further show that such deck prototypes allow the agent to predict upcoming cards with high accuracy, therefore, allowing more accurate sampling procedures for search-based agents.
Keywords
- Clustering, Deck analysis, Fuzzy multisets, Hearthstone
ASJC Scopus subject areas
- Computer Science(all)
- General Computer Science
- Mathematics(all)
- Computational Mathematics
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In: International Journal of Computational Intelligence Systems, Vol. 13, No. 1, 2020, p. 1207-1217.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Predicting cards using a fuzzy multiset clustering of decks
AU - Dockhorn, Alexander
AU - Kruse, Rudolf
PY - 2020
Y1 - 2020
N2 - Search-based agents have shown to perform well in many game-based applications. In the context of partially-observable sce-narios agent’s require the state to be fully determinized. Especially in case of collectible cards games, the sheer number of decks constructed by players hinder an agent to reliably estimate the game’s current state, and therefore, renders the search ineffective. In this paper, we propose the use of a (fuzzy) multiset representation to describe frequently played decks. Extracted deck prototypes have shown to match human expert labels well and seem to serve as an efficient abstraction of the deck space. We further show that such deck prototypes allow the agent to predict upcoming cards with high accuracy, therefore, allowing more accurate sampling procedures for search-based agents.
AB - Search-based agents have shown to perform well in many game-based applications. In the context of partially-observable sce-narios agent’s require the state to be fully determinized. Especially in case of collectible cards games, the sheer number of decks constructed by players hinder an agent to reliably estimate the game’s current state, and therefore, renders the search ineffective. In this paper, we propose the use of a (fuzzy) multiset representation to describe frequently played decks. Extracted deck prototypes have shown to match human expert labels well and seem to serve as an efficient abstraction of the deck space. We further show that such deck prototypes allow the agent to predict upcoming cards with high accuracy, therefore, allowing more accurate sampling procedures for search-based agents.
KW - Clustering
KW - Deck analysis
KW - Fuzzy multisets
KW - Hearthstone
UR - http://www.scopus.com/inward/record.url?scp=85091479000&partnerID=8YFLogxK
U2 - 10.2991/ijcis.d.200805.001
DO - 10.2991/ijcis.d.200805.001
M3 - Article
AN - SCOPUS:85091479000
VL - 13
SP - 1207
EP - 1217
JO - International Journal of Computational Intelligence Systems
JF - International Journal of Computational Intelligence Systems
SN - 1875-6891
IS - 1
ER -