Experience-Driven Procedural Content Generation

By Georgios N. Yannakakis, Julian Togelius. 2011

517 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition, Control and Systems Engineering

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Citations in the corpus

Choose Your Weapon: Survival Strategies for Depressed AI Academics (2023, background): “Procedural content generation for games was a very niche topic 15 years ago and we helped nuild a research community around it [24], [28]; recently it has become more important not only for the games industry, but also as a way to help generalize (deep) reinforcement learning [18], [23].”
Procedural Content Generation via Machine Learning (PCGML) (2017, methodology): “We can further differentiate PCGML from experience-driven PCG [12] through noting that the learned models are models of game content, not models of player experience, behavior or preference.”
Search-Based Procedural Content Generation: A Taxonomy and Survey (2011, background): “In options 2 and 3, the genotype length would grow with the desired phenotype (maze) size, but sublinearly, so that reasonably large mazes could be represented with tractably short genotypes.”
Archetypical motion: Supervised game behavior learning with Archetypal Analysis (2013, background): “Recent examples include analyzing social behavior in MMORPGs [1], analyzing players’ lifetime values [2] or modeling their experience [3], generating game content [4], or creating believable Non-Player Characters (NPCs) [5]–[7].”
Context-Aware Emotion Recognition Networks (2019, background): “Recognizing human emotions from visual contents has attracted signiﬁcant attention in numerous computer vision applications such as health care and human-computer interaction systems [1, 2, 3].”