RUN: rational ubiquitous navigation, a model for automated navigation and searching in virtual environments
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ORIGINAL ARTICLE
RUN: rational ubiquitous navigation, a model for automated navigation and searching in virtual environments Muhammad Raees1 · Sehat Ullah1 Received: 10 February 2018 / Accepted: 8 September 2020 © Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract 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. Keywords Machine learning in VR · Automated navigation · Object-based searching · Intelligent virtual reality systems
1 Introduction Virtual reality (VR) is a synthetic, immersive and interactive 3D environment. Effectiveness of a virtual environment (VE) depends on the degree of immersion it provides (Sheridan 2000; Matsas et al. 2017) and the level of naturalism it retains for interaction (Hale and Stanney 2014). With the widespread technological advancement, VR systems have shifted beyond simple audio-visual effects which were once considered enough for the simulation of real world (Dunagan and Jake 2004). The use of artificial intelligence (AI) is becoming indispensible in the domain of VR to make a virtual world indistinguishable from the real world (Lugrin et al. 2005). With the machine learning (ML) classifiers, intelligence-based interaction can be performed quite intuitively. As a result, realism of a VR system is raised. Besides * Muhammad Raees [email protected] Sehat Ullah [email protected] 1
Department of Computer Science and IT, University of Malakand, Chakdara, Pakistan
a humanoid body and virtual senses, an intelligent virtual agent (IVA) needs to have the ability to learn and respond dynamically (Aylett and Cavazza 2001). Intelligence of the IVA depends on its learning and reasoning abilities while interacting with other IVAs and responding to a VE. By dint of embedding AI algorithms in VR, an intelligent virtual reality system (IVRS) is designed wher
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