EAGER: Understanding Social Behavior in Real-Time Strategy Games

这是一项关于人们如何在动态的社会环境中做出决策的研究，通过使用在线实时战略（RTS）游戏作为研究人类行为的实验室，实现了方法论上的创新。对社交互动的调查已经在虚拟世界和在线角色扮演游戏的背景下进行了讨论，但没有在RTS游戏中进行讨论，也没有研究人员开发出数据收集技术或理论原则来研究这个以人为中心的计算的重要领域。除了娱乐价值之外，RTS游戏已经成为模拟现实世界、实时物理、场景、角色和策略的虚拟平台。特别是，多人在线即时战略游戏提供了一种新的人类互动模式，这种模式与决策理论、博弈论、规划、学习以及其他来自计算机科学、人工智能、经济学和行为科学等研究领域的概念相一致。本研究将通过三种主要方式研究RTS游戏中的用户社交策略：开发游戏环境、用户实验研究和自动学习方法。游戏将呈现给用户一系列需要完成的任务，用户将因为成功完成任务而获得积分。在早期阶段，玩家可以独自完成这些任务，但随着游戏的发展，他们将需要与他人合作。用户研究的第一阶段将包括对用户行为进行定性分析，确定用户必须在游戏中做出决定的特定点，其中社交关系将成为重要因素。在这种定性分析之后，我们将对玩家的表现进行定量分析，开发衡量每个行动收益的技术。一旦玩家开始建立强大的联盟，在做出战略决策时，一系列实验将分析他们的推理。这将包括社会联系强度的测量，结构社会网络特征，互动的过去历史，以及策略的进化模拟。研究的最后阶段将包括对用户进行控制实验，向他们展示需要考虑社会结构做出决定的情况。该项目将使软件、测试套件、文档和教学材料在Internet上免费提供。决策是人工智能中的一个重要问题，有效的决策在各种组织和实际应用中都很重要。本研究可以为开发实用的社会决策算法和应用提供理论和实验基础，使这些应用的组织和用户在决策过程中更加方便。

This is a study of how people make decisions in dynamic, social environments, achieving a methodological innovation by using online real-time strategy (RTS) games as laboratories for studying human behavior. Investigation into social interactions has been discussed in the context of virtual worlds and online role-playing games, but not in RTS games, nor have researchers yet developed the data-collection techniques or theoretical principles for doing research in this important sector of human-centered computing. In addition to their entertainment value, RTS games have emerged to become virtual platforms that simulate real-world, real-time physics, scenarios, characters, and strategies. Particularly, multi-player online RTS games are providing a new model of human interaction that is in line with decision theory, game theory, planning, learning, and other concepts from research fields such as Computer Science, Artificial Intelligence, Economics, and Behavioral Sciences.This research will study users' social strategies in RTS games in three major ways: developing a gaming environment, a user study with experiments, and an automated learning approach. The game will present users with a series of missions to be accomplished, and users will receive points for successful completion. In early stages these missions can be accomplished alone and with little effort, but as the player progresses they will require alliances with others. The first phase of user studies will involve a qualitative analysis of user behaviors, identifying specific points in the game when users must make decisions where the social relationships will be important factors. This qualitative analysis will be followed by a quantitative analysis of the players' performance, developing techniques for measuring the payoffs from each action. Once players begin developing strong alliances, a set of experiments will analyze their reasoning when making strategic decisions. This will include measurements of social tie strength, structural social network features, the past history of interactions, and evolutionary simulations of strategies. The final phase of research will involve controlled experiments with users, presenting them with situations where they have to make a decision that requires consideration of the social structure.This project will make software, test suites, documentation, and teaching materials freely available on the Internet. Decision making is an important problem in artificial intelligence, and effective decision-making is important in all kinds of organizations and real-world applications. This research could provide the theoretical and experimental basis for developing practical algorithms and applications for social decision making, to make it easier for the organizations and the users of such applications in their decision-making process.