By now, the realm of virtual reality is abuzz with high-quality visuals, enough to simulate a real-world scene. The use of intelligence in virtual reality systems, however, is a milestone yet to be achieved to make possible seamless realism in a virtual environment. This paper presents a model, rational ubiquitous navigation to improve believability of a virtual environment. The model intends to augment maturity of a virtual agent by inculcating in it the human-like learning capability. A novel approach for automated navigation and searching is proposed by incorporating machine learning in virtual reality. An intelligent virtual agent learns objects of interest along with the paths followed for navigation. A mental map is molded dynamically as a user navigates in the environment. The map is followed by the agent during self-directed navigation to access any known object. After reaching at a location where an object of interest resides, the required object is selected on the basis of front-facet feature. The model is implemented in a case-study project learn objects on path (LOOP). Twelve users evaluated the model in the immersive maze-like environment of LOOP. Results of the evaluation assure applicability of the model in various cross-modality applications.

到目前为止，虚拟现实的领域已经充斥着高质量的视觉效果，足以模拟现实世界的场景。但是，在虚拟现实系统中使用智能技术是实现虚拟环境中无缝现实性的一个里程碑。本文提出了一种模型化的，合理的普遍存在的导航，以提高虚拟环境的可信度。该模型旨在通过灌输类人的学习能力来提高虚拟代理的成熟度。通过将机器学习纳入虚拟现实，提出了一种新颖的自动导航和搜索方法。智能虚拟代理可以学习感兴趣的对象以及导航所遵循的路径。当用户在环境中导航时，心理图会动态地成型。代理在自我导向的导航过程中跟随地图，以访问任何已知对象。到达感兴趣对象所在的位置后，根据正面特征选择所需对象。该模型在案例研究项目学习路径上的对象（LOOP）中实现。十二名用户在LOOP的迷宫般沉浸式环境中评估了该模型。评估结果确保了该模型在各种交叉模式应用中的适用性。