Integrated Approach To Personalized Procedural Map Generation Using Evolutionary Algorithms

Keywords

Hierarchical optimization; interactive evolutionary computation; neuroevolution of augmenting topologies; personalized game maps; procedural content generation; recommender systems

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.

Publication Date

6-1-2015

Publication Title

IEEE Transactions on Computational Intelligence and AI in Games

Volume

7

Issue

2

Number of Pages

139-155

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/TCIAIG.2014.2341665

Socpus ID

84933505232 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84933505232

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