Predicting Cards Using a Fuzzy Multiset Clustering of Decks

Authors

Alexander Dockhorn^{1, *,} , Rudolf Kruse^2,

¹School of Electronic Engineering and Computer Science, Queen Mary University of London, Mile End Road, London, E1 4NS, United Kingdom

²Faculty of Computer Science, Otto von Guericke University Magdeburg Universitätsplatz 2, Magdeburg, 39106, Germany

^*Corresponding author. Email: a.dockhorn@qmul.ac.uk

DOI

10.2991/ijcis.d.200805.001How to use a DOI?

Keywords

Fuzzy multisets; Clustering; Deck analysis; Hearthstone

Abstract

Search-based agents have shown to perform well in many game-based applications. In the context of partially-observable scenarios 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.

Copyright

© 2020 The Authors. Published by Atlantis Press B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Alexander Dockhorn
AU  - Rudolf Kruse
PY  - 2020
DA  - 2020/08/18
TI  - Predicting Cards Using a Fuzzy Multiset Clustering of Decks
JO  - International Journal of Computational Intelligence Systems
SP  - 1207
EP  - 1217
VL  - 13
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.200805.001
DO  - 10.2991/ijcis.d.200805.001
ID  - Dockhorn2020
ER  -