2019/04/03 by Ivan Bravi, Bravi, Ivan, Simon M. Lucas +5
Computer Science · Economics, Econometrics and Finance · #Artificial Intelligence (cs.AI) #Artificial Intelligence in Games #Evolutionary Algorithms and Applications #FOS: Computer and information sciences #Sports Analytics and Performance
paper · pdf · doi:10.48550/arxiv.1904.01883
openalex publication_date 2019/04/03 · openalex created_date 2019/04/11 · openalex updated_date 2026/07/28
Game-based benchmarks have been playing an essential role in the development of Artificial Intelligence (AI) techniques. Providing diverse challenges is crucial to push research toward innovation and understanding in modern techniques. Rinascimento provides a parameterised partially-observable multiplayer card-based board game, these parameters can easily modify the rules, objectives and items in the game. We describe the framework in all its features and the game-playing challenge providing baseline game-playing AIs and analysis of their skills. We reserve to agents' hyper-parameter tuning a central role in the experiments highlighting how it can heavily influence the performance. The base-line agents contain several additional contribution to Statistical Forward Planning algorithms.