GAIG Game AI Research Group @ QMUL

Rinascimento: Optimising Statistical Forward Planning Agents for Playing Splendor

2019
Bravi, Ivan and Perez-Liebana, Diego and Lucas, Simon M and Liu, Jialin

Abstract

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.

Cite this work

@inproceedings{bravi2019rinascimento,
author= {Bravi, Ivan and Perez-Liebana, Diego and Lucas, Simon M and Liu, Jialin},
title= {{Rinascimento: Optimising Statistical Forward Planning Agents for Playing Splendor}},
year= {2019},
booktitle= {{IEEE Conference on Games (COG)}},
pages= {1--8},
abstract= {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. },
}

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