Search-Based Procedural Content Generation: A Taxonomy and Survey

By Julian Togelius, Georgios N. Yannakakis, Kenneth O. Stanley, Cameron Browne. 2011

779 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Sociology and Political Science, Evolutionary Computation

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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].”
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation (2017, background): “…a few endeavours to enhance the variety of linguistic expressions used in virtual environments (e.g., Orkin & Roy, 2007), nlg technology is relatively unrepresented in research on games, despite significant progress on dynamic content generation in game environments (e.g., Togelius et al., 2011).”
Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions (2019, methodology): “tudent Curriculum Learning [37], the teacher automatically selects sub-tasks that offer the highest slope of learning curve for the student. Ideas from the ﬁeld of procedural content generation (PCG) [38, 39], where methods are developed to generate diverse sets of levels 2 and other contents for ga”
Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions (2020, background): “…2017), intrinsically motivated goal exploration processes (IMGEPs) (Forestier et al., 2017), teacher-student curriculum learning (Matiisen et al., 2017), or procedural content generation (PCG) methods (usually focused on gaming) (Justesen et al., 2018; Shaker et al., 2016; Togelius et al., 2011).”
Procedural Content Generation via Machine Learning (PCGML) (2017, methodology): “…sps74@drexel.edu, mguzdial3@gatech.edu, christoffer@holmgard.org, amy.hoover@gmail.com, aisaksen@appabove.com, nealen@nyu.edu, julian@togelius.com This survey explores Procedural Content Generation via Machine Learning (PCGML), defined as the generation of game content using machine learning…”
Winning Isn't Everything: Training Human-Like Agents for Playtesting and Game AI (2019, background): “See [30], [31] for a survey of these techniques in game design.”
Experience-Driven Procedural Content Generation (2011, methodology): “EDPCG primarily focuses on bottom-up, search-based [6] approaches for generating content, which are driven by computational heuristics of player experience.”
Increasing generality in machine learning through procedural content generation (2019, methodology): “In search-based PCG (Table 1), stochastic search/optimization algorithms are used to search for good content according to some evaluation function [18].”
POET: open-ended coevolution of environments and their optimized solutions (2019, background): “The field of procedural content generation (PCG) [52, 61] also offers inspiration for generating new challenges (usually focused on gaming).”