발간년도 : [2023]
| 논문정보 |
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| 논문명(한글) |
[Vol.18, No.1] A Study on Game Strategy-based User Behavior Pattern Analysis Using Deep Learning |
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| 논문투고자 |
Jinhong Kim |
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| 논문내용 |
Recently, as large amounts of data become easier to process, companies are using data generated from
users to obtain useful implications by analyzing them as needed. In particular, game users play various
their situation and actively interact with other game elements, resulting in a large amount of user-based
data in games. Game-related data is used as data to improve the game environment, such as allowing
users to predict deviations, game play patterns, and anomalies in the game. Accordingly, this research
analyzed game strategies using game data, identified user behavior patterns, and conducted analysis using
multi-layer perceptron to analyze user behavior patterns. In addition, Game engine commonalities can be
the reason that there exists no single game engine capable of being used across all the different game
genres. Such an engine design could provide a number of benefits to the game development community.
Development time and cost can be significantly reduced, and programmers and designers can more
easily be transfered between different projects. They are convinced that general game engine components
can be found by analysing game engines across different genres, but they also consider the possibility
that this could exclude essential parts, making it less applicable and relevant when making games. |
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