SGER Collaborative Research: Persistent, Adaptive, Collaborative Synthespians

SGER 协作研究：持久的、适应性的、协作的综合者

• 批准号: 0749316
• 负责人: Michael Mateas
• 金额: --
• 依托单位: University of California-Santa Cruz
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-15 至 2009-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0749316&HistoricalAwards=false
• 关键词: SGER; Collaborative; Research; Persistent; Adaptive

This project explores the development of methodologies for populating worlds with persistent, adaptive, collaborative, believable synthetic actors, referred to as Synthespians. These methods are extensions of adaptive models of learning and planning to accommodate the complex, dynamic environments in massive multi-player online games. The intellectual merit includes the development and evaluation of:1. A behavior development language, with discovery, machine learning, and adaptation of behaviors directly integrated into the language, allowing for the rapid development and deployment of Synthespians.2. A framework for the actors to recognize and discover plans by observing and modeling the activities of the other agents.An expected outcome of this research is the ability to author complex virtual worlds with many participants that support intelligent and effective interaction between people and machines.Broader Impact: A scientific understanding of how we interact with each other and collaborate will benefit from our ability to simulate complex environments with dynamic and evolving individual and group behaviors. In this project, building and modeling such environments and behaviors is done within a gaming context. This work will in the long run effect and change the fields of education and entertainment. In addition, being able to model large collaborative and interactive scenarios will also help us understand and model large social dynamics phenomenon of interest to sociologists and economists.

这个项目探索了用持久的、适应性的、协作的、可信的合成演员（被称为合成演员）来填充世界的方法的发展。这些方法是学习和规划的自适应模型的扩展，以适应大型多人在线游戏中的复杂，动态的环境。智力价值包括以下方面的发展和评价：1。一种行为开发语言，将行为发现、机器学习和适应直接集成到语言中，从而允许快速开发和部署Synthespanes。2.一个框架，让参与者通过观察和建模其他智能体的活动来识别和发现计划。这项研究的预期成果是能够创作出具有许多参与者的复杂虚拟世界，支持人与机器之间的智能和有效交互。科学地理解我们如何相互作用和合作，将受益于我们模拟复杂环境的能力，进化个体和群体行为。在这个项目中，构建和建模这样的环境和行为是在游戏环境中完成的。从长远来看，这项工作将影响和改变教育和娱乐领域。此外，能够对大型协作和交互场景进行建模也将有助于我们理解和建模社会学家和经济学家感兴趣的大型社会动态现象。