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.
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Journal ArticleDOI
Interactive Constrained MAP-Elites: Analysis and Evaluation of the Expressiveness of the Feature Dimensions
TL;DR: The algorithm followed by an in-depth analysis of its behaviour is presented, with the aims of evaluating the expressive range of all possible dimension combinations in several scenarios, as well as discussing their influence in the fitness landscape and in the overall performance of the mixed-initiative procedural content generation.
Proceedings ArticleDOI
Evolutionary Methods for Generating Synthetic MasterPrint Templates: Dictionary Attack in Fingerprint Recognition
TL;DR: Three techniques, namely Covariance Matrix Adaptation Evolution Strategy (CMA-ES), Differential Evolution (DE) and Particle Swarm Optimization (PSO), are explored and show that the proposed approaches performed quite well compared to the previously known MasterPrint generation methods.
Proceedings ArticleDOI
Evolving the Hearthstone Meta
Fernando de Mesentier Silva,Rodrigo Canaan,Scott Lee,Matthew C. Fontaine,Julian Togelius,Amy K. Hoover +5 more
TL;DR: Analyzing the win rate on match-ups across different decks, being played by different strategies, and proposing and evaluating metrics to serve as heuristics with which to decide which cards to target with balance changes.
Journal ArticleDOI
Automated Map Generation for the Physical Traveling Salesman Problem
TL;DR: The results presented in this paper show that CMA-ES is able to generate maps that fulfil the desired conditions, and any optimal route for these maps should differ distinctively from: 1) the optimal distance-based TSP route and 2) the route that corresponds to always approaching the nearest waypoint first.
Proceedings Article
Optimization of platform game levels for player experience
TL;DR: An approach to modelling the effects of certain parameters of platform game levels on the players' experience of the game, adapted for generation of parameterized levels using preference learning of neural networks is demonstrated.