AlphaHorizon is trained specifically for reasoning and predicting in environments where decision-making depends on incomplete information, by using probabilistic world modeling and simulation to estimate incomplete information and evaluate future outcomes. The underlying capabilities that drive performance in imperfect-information games are applicable to a wide range of real-world decision-making problems, such as autonomous robotics, self-driving vehicle reasoning, financial markets, and multi-agent decision systems. It’s been fun to test AlphaHorizon in @devfun's competition to provide a demonstration of the broader capabilities of prediction systems designed for real-world uncertainty. GG!