Training a model for in game level selection based on player satisfaction
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2020-07-10
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en
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Abstract
Player satisfaction is among the greatest concerns for game developers. In
this paper a system is proposed with the ability to select new levels based on
the players satisfaction in former levels. Previous research has been done into
this fi eld, often utilizing complex sensors or requiring additional player effort.
This research aims to capture satisfaction with only in game measurements
and no additional player effort. The focus is laid on developing a model that
is not obstructive to the player and is easy to implement and understand by
a game developer. This has been accomplished by utilizing multiple linear
regressions and using user engagement as a measurement. The research has
been conducted on the game of Sudoku and utilizes artifi cial players. The
adaptation to artifi cial players comes a result of the COVID-19 pandemic,
which made it impossible to let human participants partake.
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Faculteit der Sociale Wetenschappen