Our bots, procedural content generation systems, and player insights technology are built from a deep integration of game design practice, psychometrics, artificial intelligence, and machine learning.
We combine clustering and embedding techniques, stochastic tree search (MCTS), evolutionary computation, deep learning, preference learning and transfer learning in models of human play behavior that we call AI Personas.
AI Personas work before any player data is collected and improve as data is collected from game designers, playtesters, soft-launch and full launch, throughout the life-cycle of games.
Christoffer works with game playing agents, player modeling, and psychometrics.
Benedikte works with user experience and product development.
Lars works on all things operational, keeping modl.ai smoothly running.
Sebastian works with neural networks, evolutionary algorithms and procedural content generation. He is an Associate Professor at the IT University of Copenhagen
Julian works with methods such as stochastic tree search, hyper-heuristics, evolutionary computation, and reinforcement learning. He is an Associate Professor at New York University.
Georgios works with affective computing, user modeling, preference learning, computational creativity and procedural content generation. He is a Professor at the University of Malta
