Integrated Approach to Personalized Procedural Map Generation Using Evolutionary Algorithms

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.

Affective Visualization in Virtual Reality: An Integrative Review

“…Finally, it is possible to use a programmatic approach to create virtual reality content in real-time, using procedural content generation (PCG) (Yannakakis and Togelius, 2011;Raffe et al, 2015;Bermudez i Badia et al, 2019;Semertzidis et al, 2020). PCG allows to create content dynamically that adjusts to user feedback.…”

“…Additional studies indicate that it is possible to use PCG to create content dynamically, adjusting it to the preferences of the user. This approach is known as experience-driven procedural content generation (EDPCG) (Yannakakis and Togelius, 2011;Raffe et al, 2015). In broad terms, EDPCG consists of an iterative process where the content is constantly modified based on the user's feedback.…”

“…Overall, the process consists of merging features from two images to create a third image. This approach could be combined with EDPCG (Yannakakis and Togelius, 2011;Raffe et al, 2015) to create personalized affective visualizations. The process would involve at least three steps: 1) Create a set of VR content that all users will observe and used that content as a baseline.…”

Evaluating the Impact of Procedurally Generated Content on Game Immersion

“…There are exceptions, for example Raffe et al (2015) compare the playability of optimised levels against that of random levels. However, there is still an unanswered question as to whether the computational cost of deploying evolutionary algorithms has any benefits in terms of meeting the goals of PCG in terms of the efficient creation of engaging content.…”