Rinascimento: Optimising Statistical Forward Planning Agents for Playing Splendor
Rinascimento: Optimising Statistical Forward Planning Agents for Playing Splendor
复制标题
Rinascimento：优化统计前瞻性规划代理以发挥辉煌
DOI：
--
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Jialin Liu
中科院分区：
文献类型：
--
作者：
Ivan Bravi;S. Lucas;Diego Perez Liebana;Jialin Liu
基于游戏的基准测试在人工智能（AI）技术的发展中发挥着至关重要的作用。提供多样化的挑战对于推动研究创新和理解现代技术至关重要。Rinascimento提供了一个参数化的部分可观察的多人纸牌游戏，这些参数可以很容易地修改游戏中的规则，目标和物品。我们描述了框架的所有功能和游戏挑战，提供基线游戏AI和分析他们的技能。我们保留代理的超参数调整在实验中的核心作用，突出它如何可以严重影响性能。基线代理包含对统计正向规划算法的几个附加贡献。
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 mul-tiplayer 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.
通用视频游戏人工智能：用于评估代理、游戏和内容生成算法的多轨框架
影响因子：
2.3
作者：
Perez-Liebana, Diego;Liu, Jialin;Khalifa, Ahmed;Gaina, Raluca D.;Togelius, Julian;Lucas, Simon M.
通讯作者：
Lucas, Simon M.