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定的研究，通过使用在线实时策略(RTS)游戏作为研究人类行为的实验室来实现方法创新。对社交互动的研究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，但还没有在RTS游戏中讨论，研究人员也还没有开发出数据收集技术或理论原理来在这一以人为中心的计算的重要领域进行研究。除了娱乐价值，RTS游戏已经成为模拟真实世界、实时物理、场景、角色和策略的虚拟平台。特别是，多人在线RTS游戏提供了一种新的人类交互模型，符合决策理论、博弈论、规划、学习等计算机科学、人工智能、经济学和行为科学等研究领域的概念。本研究将从三个主要方面研究用户在RTS游戏中的社交策略：开发游戏环境、用户实验研究和自动学习方法。这款游戏将为用户呈现一系列需要完成的任务，成功完成的用户将获得积分。在早期阶段，这些任务可以单独完成，几乎不费力气，但随着玩家的进步，他们将需要与其他人结盟。用户研究的第一阶段将涉及对用户行为的定性分析，确定用户在游戏中必须做出哪些社会关系将是重要因素的决定的特定点。在定性分析之后，将对运动员的表现进行定量分析，开发出衡量每一项行动的回报的技术。一旦玩家开始发展强大的联盟，一系列实验将在做出战略决策时分析他们的推理。这将包括对社会纽带强度的测量、结构性社会网络特征、过去的互动历史以及战略的进化模拟。研究的最后阶段将涉及与用户的受控实验，向他们展示他们必须做出需要考虑社会结构的决定的情况。该项目将使软件、测试套件、文档和教学材料在互联网上免费获得。决策是人工智能中的一个重要问题，而有效的决策在各种组织和现实世界的应用中都是重要的。本研究可以为开发实用的社会决策算法和应用程序提供理论和实验依据，使组织和应用程序的用户在决策过程中更容易。

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.