Research Groups
This page lists academic and industry research groups that solve game related problems with CI techniques.
BUGRD Bradford University Games Research & Development
Within the School of Informatics at the University of Bradford, games play a significant part in both teaching and research activities. BUGRD has been teaching about games since 1998 and their Interactive Systems and Video Games Design BSc has been running since 2000, looking at both the creative and technical aspects of game design and development. Research at BUGRD focuses mainly on AI and interaction.
ECSL Evolutionary Computing Systems Lab @ UNR
The Evolutionary Computing Systems Lab at the University of Nevada-Reno investigates systems that combine genetic algorithm (GA) search with case-based reasoning (CBR) principles. Such Case Injected Genetic AlgoRithms (CIGAR) lead to a new paradigm for machine learning with special emphasis on design, optimization, and human modeling. CIGAR learns to take less time and produce better quality solutions as it gains experience. Empirical results from logic design, combinatorial optimization, and other applications show that these systems perform remarkably well across a variety of problem domains and solution representations.
EPLEX Evolutionary Complexity Research Group @ UCF
The Evolutionary Complexity Research Group at the University of Central Florida focuses on abstracting the essential properties of natural evolution that made it possible to discover astronomically complex structures such as the human brain. If such properties can be abstracted into computer algorithms, then they can be leveraged to automate the discovery of large-scale neural networks, robot morphologies, building and vehicle architectures, art, and music.
NNRG Neural Networks Research Group @ UT Austin
The UTCS Neural Networks Research Group was founded in 1992 by Prof. Risto Miikkulainen. The group is part of the Artificial Intelligence Lab in the Computer Science Department at the University of Texas at Austin. Our research concentrates on cognitive science, computational neuroscience, and evolutionary computation, including natural language processing, episodic memory, concept and schema learning, the visual cortex, and evolving neural networks in sequential decision tasks such as robotics, game playing, and resource optimization.