Adapting models of visual aesthetics for personalized content creation

Antonios Liapis, Georgios N. Yannakakis, Julian Togelius

    Research output: Contribution to journalArticlepeer-review


    This paper introduces a search-based approach to personalized content generation with respect to visual aesthetics. The approach is based on a two-step adaptation procedure where: 1) the evaluation function that characterizes the content is adjusted to match the visual aesthetics of users; and 2) the content itself is optimized based on the personalized evaluation function. To test the efficacy of the approach, we design fitness functions based on universal properties of visual perception, inspired by psychological and neurobiological research. Using these visual properties, we generate aesthetically pleasing 2-D game spaceships via neuroevolutionary constrained optimization and evaluate the impact of the designed visual properties on the generated spaceships. The offline generated spaceships are used as the initial population of an interactive evolution experiment in which players are asked to choose spaceships according to their visual taste: the impact of the various visual properties is adjusted based on player preferences and new content is generated online based on the updated computational model of visual aesthetics of the player. Results are presented that show the potential of the approach in generating content which is based on subjective criteria of visual aesthetics.

    Original languageEnglish (US)
    Article number6185648
    Pages (from-to)213-228
    Number of pages16
    JournalIEEE Transactions on Computational Intelligence and AI in Games
    Issue number3
    StatePublished - 2012


    • Computational aesthetics
    • constrained optimization
    • experience-driven procedural content generation (EDPCG)
    • interactive evolution

    ASJC Scopus subject areas

    • Software
    • Control and Systems Engineering
    • Artificial Intelligence
    • Electrical and Electronic Engineering


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