Modeling Caribou Migrations and Traditional Hunting Strategies in a Virtual World Simulation

在虚拟世界模拟中对驯鹿迁徙和传统狩猎策略进行建模

• 批准号: 1744367
• 负责人: John O'Shea
• 金额: $ 16.88万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1744367&HistoricalAwards=false
• 关键词: Modeling; Caribou; Migrations; Traditional; Hunting

Caribou are one of the most significant animals in the Arctic. They are intimately tied to the people, history, and ecology of the region. This project will bring together archaeologists, computer scientists, geographers, ecologists and Alaska Native rights holders to investigate caribou migrations prior to the dramatic reduction in caribou numbers during the 18th Century. A unique aspect of this project is the creation of a virtual world model of the past environment for a portion of ancient Alaska. The researchers will then ask experienced Alaska Native hunters to enter the virtual world to seek migrating caribou under different conditions. Using virtual reality googles and similar equipment, the hunters will be able to experience the model world as if they were actually there. By following their movements and listening to their descriptions the researchers will be able tell how well the model is simulating reality. By comparing the model with the hunting locations chosen by ancient hunters known from archaeology, the research team hopes to see how conditions of the environment and of caribou numbers have changed over time. In addition to informing us about the past, the model will provide a valuable new tool for ecologists to think about caribou migrations and how they might be altered in response to future oil and gas development within the region. Beyond the scientific value of the research, the inclusion of Alaska Native communities in the design process will provide an opportunity for them to inform the model. The gaming format developed from the model will provide a valuable (and easily accessible) educational tool for K-12 students in Alaska, encourage interest in science and technology (STEM) among Alaska Native students, and provide opportunities for the involvement citizen scientists more broadly.In consultation with Alaska Native communities and local experts the research team will adapt a virtual world model of caribou migration developed for the Early Holocene period in the Great Lakes region and apply it to a portion of modern Alaska. The present proposal has two main goals. To allow the research team to become acquainted with the Arctic context of the research and to establish the necessary collaborative contacts particularly among Alaska Native scholars, to ground the virtual world simulation in Alaska. Second, to recruit one or more Alaska Native hunters to experience the current virtual world simulation via an immersive augmented reality system. The placing of knowledgeable hunters within a virtual world simulation as a means of exploring the interactions of human actors in modeled environments is an emerging approach to behavioral research in many fields, and this will be among the first systematic applications of the approach to anthropology. Through these activities, the research team will verify the basic elements of the virtual world simulation and fine tune the augmented reality system for recording the movements and comments of the hunters as they traverse the model landscape. These results will also provide important data for better understanding the Early Holocene occupation of the Great Lakes region and will highlight differences in hunting strategies and tactics observed in recent and ancient times. The work will also lay the foundation for a systematic remote sensing survey of built hunting structures (both recent and ancient) in an archaeologically under-research portion of Alaska.

驯鹿是北极中最重要的动物之一。他们与该地区的人民，历史和生态密切相关。该项目将汇集考古学家，计算机科学家，地理学家，生态学家和阿拉斯加的本土权利持有者，以调查驯鹿迁移，然后在18世纪的驯鹿数量急剧减少。该项目的一个独特方面是为古代阿拉斯加一部分的过去环境的虚拟世界模型创建。 然后，研究人员将要求经验丰富的阿拉斯加本地猎人进入虚拟世界，以寻求在不同条件下移民北非驯鹿。 使用虚拟现实的Google和类似设备，猎人将能够体验模型世界，就好像它们实际上在那里一样。 通过遵循他们的动作并聆听他们的描述，研究人员将能够说明模型对现实的模拟程度。 通过将模型与古老的猎人从考古学选择的狩猎地点进行比较，研究小组希望看到环境和驯鹿数量的条件随着时间的流逝而发生变化。除了通知我们过去，该模型还将为生态学家提供一个有价值的新工具，以考虑驯鹿迁移以及如何根据该地区未来的石油和天然气开发来改变它们。除了研究的科学价值之外，在设计过程中纳入了阿拉斯加土著社区，这将为他们提供一个告知模型的机会。该模型开发的游戏格式将为阿拉斯加的K-12学生提供有价值的（易于获得的）教育工具，鼓励对阿拉斯加土著学生的科学和技术（STEM）的兴趣，并为参与公民科学家提供更多的机会，以更广泛的咨询。在与阿拉斯加的本地人社区和当地专家相关方面，研究团队将为一项较早的迁移量的批准，以适用于较早的迁移，以适用于较早的迁移率，以适用于较早的洛克内。现代阿拉斯加。 本提案有两个主要目标。 为了让研究团队熟悉研究的北极环境，并建立了必要的合作联系，尤其是在阿拉斯加本地学者中，以基础阿拉斯加的虚拟世界模拟。 其次，招募一个或多个阿拉斯加本地猎人通过沉浸式增强现实体系体验当前的虚拟世界模拟。 将知识渊博的猎人放置在虚拟世界模拟中，作为探索人类参与者在建模环境中相互作用的一种手段，是许多领域的行为研究的新兴方法，这将是人类学方法的第一个系统应用之一。通过这些活动，研究团队将验证虚拟世界模拟的基本要素，并微调增强现实系统，以记录猎人穿越模型景观时的运动和评论。 这些结果还将提供重要的数据，以更好地了解大湖地区的全新世早期职业，并将突出最近和远古时代观察到的狩猎策略和策略的差异。 这项工作还将为在阿拉斯加的考古底部研究部分中对建筑狩猎结构（最近和古代）进行系统的遥感调查奠定基础。

项目成果

The Deep Dive System: Reimaging Global Climate Change with Artificial Intelligence and Virtual Reality

深潜系统：利用人工智能和虚拟现实重新想象全球气候变化

• 发表时间: 2023
• 期刊: CA
• 作者: Saad, S.

Learning to Evolve Procedural Content in Games Using Cultural Algorithms

学习使用文化算法演化游戏中的程序内容

• DOI: 10.1109/transai54797.2022.00026
• 发表时间: 2022
• 期刊: IEEE
• 作者: Saad, Sarah;Palazzolo, Thomas;Zhang, Chencheng;Reynolds, Robert G.;Lemke, Ashley;Shea, John O';O'Shea, Cailen
• 通讯作者: O'Shea, Cailen

Extraordinary Claims Require Extraordinary Evidence: The Role of Inter-Laboratory Collaboration in Archaeological Discovery

非凡的主张需要非凡的证据：实验室间合作在考古发现中的作用

• 发表时间: 2022
• 期刊: Obsidian Across the Americas: Compositional Studies Conducted in the Elemental Analysis Facility at the Field Museum of Natural History
• 作者: Riebe, D.

Using Cooperative Multi-agent Systems to Support the Generation of Student Learning Profiles in the Deep Dive Land Bridge System

使用协作多智能体系统支持 Deep Dive Land Bridge 系统中学生学习档案的生成

• 发表时间: 2022
• 期刊: Proceedings
• 作者: Zhang, C.

Incorporating the Dynamics of Climate Change into the Deep Dive Virtual Reality Underwater Site Prediction System

将气候变化动态纳入深潜虚拟现实水下地点预测系统

• 发表时间: 2023
• 期刊: Human-Computer Interaction International 2023
• 作者: Saad, S.