The multiplayer online battle arena (MOBA) genre has gained significant
popularity and economic success, attracting considerable research interest
within the Human-Computer Interaction community. Enhancing the gaming
experience requires a deep understanding of player behavior, and a crucial
aspect of MOBA games is matchmaking, which aims to assemble teams of comparable
skill levels. However, existing matchmaking systems often neglect important
factors such as players' position preferences and team assignment, resulting in
imbalanced matches and reduced player satisfaction. To address these
limitations, this paper proposes a novel framework called CUPID, which
introduces a novel process called ``re-matchmaking'' to optimize team and
position assignments to improve both fairness and player satisfaction. CUPID
incorporates a pre-filtering step to ensure a minimum level of matchmaking
quality, followed by a pre-match win-rate prediction model that evaluates the
fairness of potential assignments. By simultaneously considering players'
position satisfaction and game fairness, CUPID aims to provide an enhanced
matchmaking experience. Extensive experiments were conducted on two
large-scale, real-world MOBA datasets to validate the effectiveness of CUPID.
The results surpass all existing state-of-the-art baselines, with an average
relative improvement of 7.18% in terms of win prediction accuracy. Furthermore,
CUPID has been successfully deployed in a popular online mobile MOBA game. The
deployment resulted in significant improvements in match fairness and player
satisfaction, as evidenced by critical Human-Computer Interaction (HCI) metrics
covering usability, accessibility, and engagement, observed through A/B
testing. To the best of our knowledge, CUPID is the first re-matchmaking system
designed specifically for large-scale MOBA games.

提供了一个名为 CUPID 的新框架，利用重新匹配实现了职位和队伍的优化分配，从而提高公平性和玩家满意度。在真实 MOBA 数据集的实验中，CUPID 的效果超越所有基准模型，以平均相对改进 7.18% 的胜率预测准确性。此外，CUPID 已经成功应用于一款受欢迎的在线移动 MOBA 游戏，显著改善了比赛的公平性和玩家满意度。

CUPID：在线 MOBA 游戏通过再匹配系统提高战斗公平性和位置满意度

CUPID: Improving Battle Fairness and Position Satisfaction in Online  MOBA Games with a Re-matchmaking System

Games have always been popular testbeds for Artificial Intelligence (AI). In
the last decade, we have seen the rise of the Multiple Online Battle Arena
(MOBA) games, which are the most played games nowadays. In spite of this, there
are few works that explore MOBA as a testbed for AI Research. In this paper we
present and discuss the main features and opportunities offered by MOBA games
to Game AI Research. We describe the various challenges faced along the game
and also propose a discrete model that can be used to better understand and
explore the game. With this, we aim to encourage the use of MOBA as a novel
research platform for Game AI.

本文探讨了在 MOBA 游戏中使用人工智能进行游戏研究的主要特征和机会，并提出了一种离散模型，以更好地理解和探索游戏，以此来鼓励 MOBA 作为新的游戏人工智能研究平台的使用。

MOBA：游戏人工智能的新战场

MOBA: a New Arena for Game AI

Successful analysis of player skills in video games has important impacts on
the process of enhancing player experience without undermining their continuous
skill development. Moreover, player skill analysis becomes more intriguing in
team-based video games because such form of study can help discover useful
factors in effective team formation. In this paper, we consider the problem of
skill decomposition in MOBA (MultiPlayer Online Battle Arena) games, with the
goal to understand what player skill factors are essential for the outcome of a
game match. To understand the construct of MOBA player skills, we utilize
various skill-based predictive models to decompose player skills into
interpretative parts, the impact of which are assessed in statistical terms. We
apply this analysis approach on two widely known MOBAs, namely League of
Legends (LoL) and Defense of the Ancients 2 (DOTA2). The finding is that base
skills of in-game avatars, base skills of players, and players'
champion-specific skills are three prominent skill components influencing LoL's
match outcomes, while those of DOTA2 are mainly impacted by in-game avatars'
base skills but not much by the other two.

通过使用不同的技能预测模型，该研究分解了 MOBA 游戏玩家技能并将其影响以统计术语评估，发现在 LOL 中，游戏人物的基本技能、玩家的基本技能和玩家特定的技能是影响赛果的三个主要技能组成部分，而 DOTA2 的赛果主要受游戏角色的基本技能影响，而不会很受其他两种技能影响。