Game State Evaluation Heuristics in General Video Game Playing
Game State Evaluation Heuristics in General Video Game Playing
复制标题
一般视频游戏中的游戏状态评估启发法
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
H. Bernardino
中科院分区：
文献类型：
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作者：
Bruno Santos;H. Bernardino
在一般游戏（GGP）中，人工智能方法玩各种各样的游戏。通用视频游戏AI竞赛（GVGAI）是最着名的GGP竞赛之一，控制器在Atari 2600控制台的启发下测量他们的游戏性能。这里使用GVGAI框架。在游戏中，控制器可以执行模拟来制定其游戏计划，识别可能的结果状态的胜利/失败的机会是决策的基本特征。在GVGAI中，创建适当的评估标准是一个挑战，因为算法没有关于游戏的先前信息，例如获胜条件和分数奖励。我们在这里建议使用（i）由游戏提供的化身相关信息，（ii）空间探索鼓励和（iii）在游戏过程中获得的知识，以提高游戏状态的评估。此外，还采取了惩罚办法。一项研究，这些技术结合两个GVGAI算法，即滚动时域进化算法（RHEA）和蒙特卡洛树搜索（MCTS）。使用20确定性和随机游戏进行计算实验，并将所提出的方法所获得的结果与其基线技术和文献中的其他方法所发现的结果进行比较。我们观察到，所提出的技术（i）提出了更多的胜利和F1分数比他们的原始版本和（ii）获得竞争力的解决方案相比，从文献中找到的方法。
In General Game Playing (GGP), artificial intelligence methods play a diverse set of games. The General Video Game AI Competition (GVGAI) is one of the most famous GGP competitions, where controllers measure their performance in games inspired by the Atari 2600 console. Here, the GVGAI framework is used. In games where the controller can perform simulations to develop its game plan, recognizing the chance of victory/defeat of the possible resulting states is an essential feature for decision making. In GVGAI, the creation of appropriate evaluation criteria is a challenge as the algorithm has no previous information regarding the game, such as win conditions and score rewards. We propose here the use of (i) avatar-related information provided by the game, (ii) spacial exploration encouraging and (iii) knowledge obtained during gameplay in order to enhance the evaluation of game states. Also, a penalization approach is adopted. A study is presented where these techniques are combined with two GVGAI algorithms, namely, Rolling Horizon Evolutionary Algorithm (RHEA) and Monte Carlo Tree Search (MCTS). Computational experiments are performed using 20 deterministic and stochastic games, and the results obtained by the proposed methods are compared to those found by their baseline techniques and other methods from the literature. We observed that the proposed techniques (i) presented a larger number of wins and F1-Scores than those found by their original versions and (ii) obtained competitive solutions when compared to those found by methods from the literature.