J
Julian Togelius
Researcher at New York University
Publications - 442
Citations - 15850
Julian Togelius is an academic researcher from New York University. The author has contributed to research in topics: Game design & Game mechanics. The author has an hindex of 58, co-authored 420 publications receiving 13135 citations. Previous affiliations of Julian Togelius include Dalle Molle Institute for Artificial Intelligence Research & Harvard University.
Papers
More filters
Book ChapterDOI
Evolving game-specific UCB alternatives for general video game playing
TL;DR: Genetic programming is used to evolve replacements to the UCB1 equation targeted at playing individual games in the General Video Game AI (GVGAI) Framework to create an evolved portfolio of UCB variations that could be useful for a hyper-heuristic game-playing agent.
Posted Content
DeepMasterPrints: Generating MasterPrints for Dictionary Attacks via Latent Variable Evolution
TL;DR: DeepMasterPrints as mentioned in this paper is based on training a Generative Adversarial Network (GAN) on a set of real fingerprint images and then searching for latent input variables to the generator network that can maximize the number of impostor matches as assessed by a fingerprint recognizer.
Proceedings Article
Decision making styles as deviation from rational action: a super Mario case study
TL;DR: A method of modeling play styles as deviations from approximations of game theoretically rational actions is described, interpreted as containing information about player skill and player decision making style that is useful for differentiating between players and for understanding why human player behavior is attributed intentionality.
Proceedings ArticleDOI
Sensorless but not Senseless: Prediction in Evolutionary Car Racing
TL;DR: It is found that predictors with good driving performance do not sample the set of predictors which minimize the prediction error in the sensors.
Proceedings ArticleDOI
Can you feel it?: evaluation of affective expression in music generated by MetaCompose
TL;DR: The hypothesis that people can recognize changes in music mood and that MetaCompose can express perceptibly different levels of arousal is confirmed, while it is mainly perceived as expected, changes in arousal seems to also influence perceived valence.