Machine Learning Algorithms for Player Satisfaction Optimization
Date Issued
2012
Author(s)
Bojkovski, Nenad
Madevska Bogdanova, Ana
Abstract
There are several state-of-the-art algorithms currently used
for optimization of various aspects of games affecting player
satisfaction. In this paper we give a survey of these methods
in order to present the platform of research for modeling
player satisfaction for a generic player. We focus on the
systems for optimization of overall player experience possible
applicable on more genres of games. The algorithms are used
for optimization of Non-Player Characters (NPC) behavior,
Content Generation, Dynamic Difficulty Adjustment (DDA)
etc.
for optimization of various aspects of games affecting player
satisfaction. In this paper we give a survey of these methods
in order to present the platform of research for modeling
player satisfaction for a generic player. We focus on the
systems for optimization of overall player experience possible
applicable on more genres of games. The algorithms are used
for optimization of Non-Player Characters (NPC) behavior,
Content Generation, Dynamic Difficulty Adjustment (DDA)
etc.
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