Julian Togelius focuses on Artificial intelligence, Evolutionary computation, Evolutionary algorithm, Game design and Game mechanics. His research on Artificial intelligence frequently connects to adjacent areas such as Machine learning. His Machine learning research includes themes of Taxonomy, Field, Information retrieval and Search algorithm.
His Evolutionary computation study incorporates themes from Theoretical computer science and Content creation. His Evolutionary algorithm study combines topics in areas such as Fitness function, Pareto principle, Representation, Player experience and Crossover. His study in the fields of Video game design under the domain of Game mechanics overlaps with other disciplines such as Mathematical game.
Julian Togelius spends much of his time researching Artificial intelligence, Human–computer interaction, Machine learning, Game design and Evolutionary computation. His work in Artificial intelligence tackles topics such as Video game which are related to areas like Set. Particularly relevant to Level design is his body of work in Human–computer interaction.
His work on Game design document and Game art design as part of general Game design study is frequently linked to Sequential game and Simulations and games in economics education, bridging the gap between disciplines. As a member of one scientific family, Julian Togelius mostly works in the field of Evolutionary computation, focusing on Theoretical computer science and, on occasion, Space. Julian Togelius interconnects Video game development and Game Developer in the investigation of issues within Game mechanics.
His primary areas of study are Artificial intelligence, Machine learning, Human–computer interaction, Video game and Content generation. Julian Togelius regularly links together related areas like Domain in his Artificial intelligence studies. His Machine learning research focuses on subjects like Classifier, which are linked to Adversarial system.
Julian Togelius combines subjects such as Theoretical computer science, Game mechanics, Solver, Integer programming and Component with his study of Video game. His studies deal with areas such as Space and Computer graphics as well as Game mechanics. His work in Generator addresses subjects such as Level design, which are connected to disciplines such as Evolutionary computation.
Julian Togelius mainly focuses on Artificial intelligence, Content generation, Theoretical computer science, Reinforcement learning and Range. His Artificial intelligence research incorporates elements of Domain, Machine learning and Quality. His work in the fields of Machine learning, such as Overfitting, intersects with other areas such as Generality.
His studies in Theoretical computer science integrate themes in fields like Game mechanics, Translation and Dimension. Julian Togelius has included themes like Level design, Representation and Rotation in his Reinforcement learning study. The concepts of his Deep learning study are interwoven with issues in Game design, Multimedia, Artificial neural network and Unsupervised learning.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Search-Based Procedural Content Generation: A Taxonomy and Survey
J. Togelius;G. N. Yannakakis;K. O. Stanley;C. Browne.
IEEE Transactions on Computational Intelligence and AI in Games (2011)
Procedural Content Generation in Games
Noor Shaker;Julian Togelius;Mark J. Nelson.
Experience-Driven Procedural Content Generation
G. N. Yannakakis;J. Togelius.
IEEE Transactions on Affective Computing (2011)
Artificial Intelligence and Games
Georgios N. Yannakakis;Julian Togelius.
Towards automatic personalised content creation for racing games
J. Togelius;R. De Nardi;S.M. Lucas.
computational intelligence and games (2007)
An experiment in automatic game design
J. Togelius;J. Schmidhuber.
computational intelligence and games (2008)
Modeling Player Experience for Content Creation
C. Pedersen;J. Togelius;G.N. Yannakakis.
IEEE Transactions on Computational Intelligence and AI in Games (2010)
Towards automatic personalized content generation for platform games
Noor Shaker;Georgios Yannakakis;Julian Togelius.
national conference on artificial intelligence (2010)
Procedural Content Generation via Machine Learning (PCGML)
Adam Summerville;Sam Snodgrass;Matthew Guzdial;Christoffer Holmgard.
IEEE Transactions on Games (2018)
The 2014 General Video Game Playing Competition
Diego Perez-Liebana;Spyridon Samothrakis;Julian Togelius;Tom Schaul.
IEEE Transactions on Computational Intelligence and AI in Games (2016)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: