DOP: Discover Objects and Paths, a model for automated navigation and selection in virtual environments

Abstract

Navigation and selection are the two interaction tasks often needed for the manipulation of an object in asynthetic world. An interface that supports automatic navigation and selection may increase the realism of a virtualreality (VR) system. Such an engrossing interface of a VR system is possible by incorporating machine learning (ML)into the realm of the virtual environment (VE). The use of intelligence in VR systems, however, is a milestone yet to beachieved to make seamless realism in a VE possible. To improve the believability of an intelligent virtual agent (IVA),this research work presents DOP (“Discover Objects and Paths”), a novel model for automated navigation and selection.The model, by intermingling ML with the VE, intends to augment the maturity of a virtual agent to the extent ofhuman-level intelligence. Using ML classifiers, an IVA learns objects of interest along with the paths leading to theobjects. To access any known object, the IVA then follows a mental map of the scene for self-directed navigation. Afterreaching a proper location in the designed VE, the required object is selected by using the ML algorithms. Extending MLto VR, the model was implemented in a case-study project called “Learn Objects on a Path” (LOOP). The application,having a maze-like VE, was evaluated in terms of accuracy and applicability by eight users. The results obtained showedthat the model can be incorporated into a number of cross-modality applications.