MOBA-Slice：一个基于时间片的MOBA博弈团队相对优势评价框架
MOBA-Slice: A Time Slice Based Evaluation Framework of Relative Advantage between Teams in MOBA Games
摘要：
多人在线对战竞技场（MOBA）是目前世界上最受欢迎的数字游戏类型之一。这些复杂的游戏中包含的知识领域很大。人类和算法很难评估实时比赛情况或预测比赛结果。在本文中，我们介绍了MOBA-Slice，一个基于时间片的评估框架之间的相对优势的MOBA游戏。MOBA-Slice是一种基于学习的定量评估方法，类似于AlphaGo的价值网络。它为进一步的MOBA相关研究奠定了基础，包括AI开发。在MOBA-Slice中，通过分析影响MOBA游戏结果的因素，设计了一个神经网络模型来拟合我们的折扣评价函数。然后，我们将MOBA-Slice应用于防御的古人2（DotA2），一个典型的和流行的MOBA游戏。大量的比赛回放的实验表明，我们的模型可以很好地工作在任意匹配。MOBA-Slice不仅在结果预测上比DotA Plus Assistant高出3.7%的准确率，还支持对比赛剩余时间的预测，进而实现对团队之间相对优势的评估。
英文摘要：
Multiplayer Online Battle Arena (MOBA) is currently one of the most popular genres of digital games around the world. The domain of knowledge contained in these complicated games is large. It is hard for humans and algorithms to evaluate the real-time game situation or predict the game result. In this paper, we introduce MOBA-Slice, a time slice based evaluation framework of relative advantage between teams in MOBA games. MOBA-Slice is a quantitative evaluation method based on learning, similar to the value network of AlphaGo. It establishes a foundation for further MOBA related research including AI development. In MOBA-Slice, with an analysis of the deciding factors of MOBA game results, we design a neural network model to fit our discounted evaluation function. Then we apply MOBA-Slice to Defense of the Ancients 2 (DotA2), a typical and popular MOBA game. Experiments on a large number of match replays show that our model works well on arbitrary matches. MOBA-Slice not only has an accuracy 3.7% higher than DotA Plus Assistant at result prediction, but also supports the prediction of the remaining time of the game, and then realizes the evaluation of relative advantage between teams.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
