General Game Playing Hyper-Agents for Ludii

This paper explores the viability and effectiveness of hyper-agent approaches for the Ludii general game system. These hyper-agents utilise trained machine learning models to predict the optimal sub-agent and heuristics for previously unseen board games, based on automatically detectable game parameters (ludemes and concepts). Several hyper-agents based on portfolio and ensemble design approaches were implemented within the Ludii system. Each hyper-agent was trained on 460 games with known sub-agent and heuristic performances, with evaluations being performed on a representative set of 50 new games. Our best performing hyper-agent approach demonstrated a statistically significant win-rate improvement over all of the individual sub-agents utilised in its training corpus.