Integrated approach to personalized procedural map generation using evolutionary algorithms

Raffe, W, Zambetta, F, Li, X and Kenneth, S 2015, 'Integrated approach to personalized procedural map generation using evolutionary algorithms', IEEE Transactions on Computational Intelligence and AI in Games, vol. 7, no. 2, pp. 139-155.


Document type: Journal Article
Collection: Journal Articles

Title Integrated approach to personalized procedural map generation using evolutionary algorithms
Author(s) Raffe, W
Zambetta, F
Li, X
Kenneth, S
Year 2015
Journal name IEEE Transactions on Computational Intelligence and AI in Games
Volume number 7
Issue number 2
Start page 139
End page 155
Total pages 17
Publisher IEEE
Abstract In this paper, we propose the strategy of integrating multiple evolutionary processes for personalized procedural content generation (PCG). In this vein, we provide a concrete solution that personalizes game maps in a top-down action-shooter game to suit an individual player's preferences. The need for personalized PCG is steadily growing as the player market diversifies, making it more difficult to design a game that will accommodate a broad range of preferences and skills. In the solution presented here, the geometry of the map and the density of content within that geometry are represented and generated in distinct evolutionary processes, with the player's preferences being captured and utilized through a combination of interactive evolution and a player model formulated as a recommender system. All these components were implemented into a test bed game and experimented on through an unsupervised public experiment. The solution is examined against a plausible random baseline that is comparable to random map generators that have been implemented by independent game developers. Results indicate that the system as a whole is receiving better ratings, that the geometry and content evolutionary processes are exploring more of the solution space, and that the mean prediction accuracy of the player preference models is equivalent to that of existing recommender system literature. Furthermore, we discuss how each of the individual solutions can be used with other game genres and content types.
Subject Virtual Reality and Related Simulation
Neural, Evolutionary and Fuzzy Computation
Keyword(s) Hierarchical optimization
interactive evolutionary computation
neuroevolution of augmenting topologies
personalized game maps
procedural content generation
recommender systems
DOI - identifier 10.1109/TCIAIG.2014.2341665
Copyright notice © 2014 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
ISSN 1943-068X
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