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Artificial Neural Network Development with Player Data for Virtual Reality Game Immersion

Artificial Neural Network Development with Player Data for Virtual Reality Game Immersion
利用玩家数据进行人工神经网络开发,实现虚拟现实游戏沉浸
批准号:
544775-2019
负责人:
Toosizadeh, Saeed
金额:
$2.51万
依托单位国家:
加拿大
项目类别:
Applied Research and Development Grants - Level 2
财政年份:
2019
资助国家:
加拿大
项目状态:
已结题
起止时间:
2019-01-01 至 2020-12-31

项目摘要

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中文摘要
翻译
莫霍克学院和沙夫茨伯里Lucid公司。(沙夫茨伯里)将合作共同设计和进一步开发,培训和测试专有的自适应游戏系统。沙夫茨伯里将把这些结果整合到他们的恐怖娱乐虚拟现实(VR)视频游戏《Lucid VR》(Lucid)中。该项目将开发一个人工神经网络(ANN)来优化玩家的游戏娱乐体验,以获得最大的“沉浸式效果”。期望的沉浸式效果是三维(3D)视频游戏中的最佳玩家参与。项目团队将通过基于ANN输出的游戏动态难度调整(DDA)来实现这一目标。玩家在游戏中的动作是不同的,需要ANN评估他们的沉浸水平和DDA,以相应地调整游戏。为了有效地做到这一点,人工神经网络(ANN),这是一种人工智能(AI)技术,需要测试和调整,开发,训练和游戏测试的迭代。目标是一个改进的人工神经网络,通过控制器运动跟踪玩家在Lucid游戏过程中的身体运动,并响应这些运动,Lucid将提供调整的游戏体验。玩家的物理控制器移动代表玩家对游戏的参与程度,并与他们的情绪反应和状态相关。沙夫茨伯里还计划使用该项目的输出来提高他们即将推出的VR游戏的玩家沉浸感。视频游戏玩家是忠诚和苛刻的,因此提供最佳的沉浸感以确保高质量的玩家(游戏玩家)参与,响应和积极的口碑非常重要。一个恐怖游戏必须娱乐和吓唬它的玩家。口碑对于视频游戏的成功至关重要,而来自参与体验的早期积极评价是商业成功的关键。加拿大娱乐软件协会报告称,2017年,加拿大视频游戏行业雇用了40,600名直接和间接全职同等职位的员工,该行业每年为国内生产总值贡献37亿美元。该项目的成果将有助于沙夫茨伯里进一步确立其在行业内的地位,进一步促进提供的就业机会和创造的GDP。
英文摘要
Mohawk College and Shaftesbury Lucid Inc. (Shaftesbury) will collaborate to co-design and further develop, train, and test a proprietary adaptive game system. Shaftesbury will integrate the results into their horror entertainment Virtual Reality (VR) video game, Lucid VR (Lucid). The project will develop an Artificial Neural Network (ANN) to optimize players' in-game entertainment experiences for the maximum 'immersive effect'. The desired immersive effect is optimum player engagement in the three-dimensional (3D) video game. The project team will achieve that by providing game Dynamic Difficulty Adjustment (DDA) based on the ANN outputs. Players' in-game motions are different and require ANN assessment of their level of immersion and DDA to adjust game play accordingly. To do this effectively, the ANN, which is an Artificial Intelligence (AI) technique, requires iterations of testing and adjustment, development, training and game testing.The goal is an improved ANN that tracks a player's physical movements via controller motions during Lucid gameplay, and in response to those motions, Lucid will provide an adjusted gameplay experience. A player's physical controller movements represent a player's level of engagement with the game and correlate to their emotional responses and state. Shaftesbury also plans to use the project's outputs to improve player immersion for their upcoming VR games. Video game players are loyal and demanding, therefore it is important to provide optimum immersion to ensure quality player (gamer) engagement, responses, and positive word of mouth. A horror game must entertain and scare its players. Word of mouth is critical to video game success, and early positive reviews from engaged experiences are key to commercial success.The Entertainment Software Association of Canada reported that in 2017, the Canadian video game industry employed 40,600 people with direct and indirect full-time equivalent positions, and the industry contributed $3.7 billion annually to the Gross Domestic Product. This project's outcomes will help Shaftesbury further establish its place within the industry to further contribute to the jobs provided and GDP created.
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Neural Process模型的多样化高保真技术研究