Machine Learning Algorithms for Player Satisfaction Optimization

Bojkovski, Nenad and Madevska-Bogdanova, Ana (2012) Machine Learning Algorithms for Player Satisfaction Optimization. In: Proceedings of the Nineth Conference on Informatics and Information Technology. Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje, Macedonia, Skopje, Macedonia, pp. 144-148. ISBN 978-608-4699-01-9

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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.

Item Type: Book Section
Subjects: International Conference on Informatics and Information Technologies > Intelligent Systems
International Conference on Informatics and Information Technologies > Robotics
International Conference on Informatics and Information Technologies > Bioinformatics
Depositing User: Vangel Ajanovski
Date Deposited: 28 Oct 2016 00:15
Last Modified: 28 Oct 2016 00:15
URI: http://eprints.finki.ukim.mk/id/eprint/11227

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