This paper describes the analysis and clustering of motion trajectories obtained while users navigate within a virtual environment (VE). It presents a neural network simulation that produces a set of five clusters which help to differentiate users on the basis of efficient and inefficient navigational strategies. The accuracy of classification carried out with a self-organising map algorithm was tested and improved to in excess of 85% by using learning vector quantisation. This paper considers how such user classifications could be utilised in the delivery of intelligent navigational support and the dynamic reconfiguration of scenes within such VEs. We explore how such intelligent assistance and system adaptivity could be delivered within a Multi-Agent Systems (MAS) context.
The work pioneers the exploration of machine learning potential for developing adaptive virtual environments to support spatial reasoning. It proposes an original machine-learning based methodology for automatic and real time discrimination of different groups of users based on their spatial behaviour. This journal paper is an extended version of the one presented at a workshop at the International Conference on Computational Science 2003 published in LNCS 2659. RAE_import_type : Journal article RAE_uoa_type : Computer Science and Informatics
