Ivan Bravi

Ivan Bravi is a Visiting Professor at Queen Mary University of London's School of Electronic Engineering and Computer Science. His research focuses on artificial intelligence, particularly in the context of game AI and automated playtesting. He explores topics such as statistical forward planning, reinforcement learning, and evolutionary optimization for game agents.

Bravi's work emphasizes developing intelligent systems capable of navigating complex game environments, including card games like Splendor and unforgiving grid-based simulations like the Game of Life. His contributions include the Rinascimento framework, which employs event-value functions and hyperparameter tuning to enhance agent decision-making.

His publications highlight advancements in agent optimization, behavioral expressivity, and self-adaptive algorithms. While no awards are listed, his active research in game AI underscores a commitment to pushing boundaries in computational decision-making and adaptive learning.

Bravi collaborates with academic and industry partners to refine AI-driven solutions for gaming and beyond, leveraging interdisciplinary approaches within the School's vibrant research ecosystem.