Player Style Clustering without Game Variables
| Autor: | Daniel Kudenko, Sam Devlin, James Alfred Walker, Mark Ferguson |
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| Rok vydání: | 2020 |
| Předmět: |
Focus (computing)
Source code business.industry Computer science media_common.quotation_subject 05 social sciences Frame (networking) ComputingMilieux_PERSONALCOMPUTING 050801 communication & media studies 020207 software engineering 02 engineering and technology Machine learning computer.software_genre Base (topology) Field (computer science) 0508 media and communications 0202 electrical engineering electronic engineering information engineering Artificial intelligence Cluster analysis business computer Encoder Feature learning media_common |
| Zdroj: | FDG |
| DOI: | 10.1145/3402942.3402960 |
| Popis: | Player clustering when applied to the field of video games has several potential applications. For example, the evaluation of the composition of a player base or the generation of AI agents with identified playing styles. These agents can then be used for either the testing of new game content or used directly to enhance a player’s gaming experience. Most current player clustering techniques focus on the use of internal game variables. This raises two main issues: (1) the availability of game variables, as source code access is required to log them and hence limits the data sources that can be used, and (2) the choice of game variables can introduce unintended bias in the types of play style extracted. In this work, a hybrid unsupervised frame encoder and a ‘reference-based’ clustering algorithm are both proposed and combined to allow clustering from raw game play videos. It is shown that the proposed methods are most beneficial when the types of play styles are unknown. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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