fdata-02-00014-g0004_Deep Neural Networks for Optimal Team Composition.tif

Cooperation is a fundamental social mechanism, whose effects on human performance have been investigated in several environments. Online games are modern-days natural settings in which cooperation strongly affects human behavior. Every day, millions of players connect and play together in team-based games: the patterns of cooperation can either foster or hinder individual skill learning and performance. This work has three goals: (i) identifying teammates' influence on players' performance in the short and long term, (ii) designing a computational framework to recommend teammates to improve players' performance, and (iii) setting to demonstrate that such improvements can be predicted via deep learning. We leverage a large dataset from Dota 2, a popular Multiplayer Online Battle Arena game. We generate a directed co-play network, whose links' weights depict the effect of teammates on players' performance. Specifically, we propose a measure of network influence that captures skill transfer from player to player over time. We then use such framing to design a recommendation system to suggest new teammates based on a modified deep neural autoencoder and we demonstrate its state-of-the-art recommendation performance. We finally provide insights into skill transfer effects: our experimental results demonstrate that such dynamics can be predicted using deep neural networks.

合作是一项基础性社会机制，其对人类行为表现的影响已在多种场景中得到广泛研究。在线游戏是当代展现合作对人类行为影响的天然场域：每日有数以百万计的玩家组队参与团队类联机游戏，合作模式既会促进个体的技能学习与表现提升，也可能阻碍这一进程。
本研究共设定三项核心目标：(i) 明确队友在短期与长期维度对玩家表现的影响效应；(ii) 设计一套计算框架，以实现能够提升玩家表现的队友推荐；(iii) 验证此类表现提升可通过深度学习（deep learning）模型进行预测。
我们采用源自热门多人在线战术竞技游戏（Multiplayer Online Battle Arena）《Dota 2》的大规模数据集，构建了有向共玩网络，其边权重用于表征队友对玩家表现的影响强度。具体而言，我们提出一种网络影响度量方法，可捕捉玩家间随时间推移的技能传递过程。基于该建模框架，我们利用改进的深度神经自编码器（deep neural autoencoder）设计了队友推荐系统，并验证其推荐性能达到当前最优水准。最后我们针对技能传递效应展开分析：实验结果表明，此类动态交互过程可通过深度神经网络进行有效预测。