Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Self-Organizing Maps
Spatial Cognition, An Interdisciplinary Approach to Representing and Processing Spatial Knowledge
Spatial Cognition, An Interdisciplinary Approach to Representing and Processing Spatial Knowledge
Using Adaptive Tracking to Classify and Monitor Activities in a Site
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Application of the Self-Organizing Map to Trajectory Classification
VS '00 Proceedings of the Third IEEE International Workshop on Visual Surveillance (VS'2000)
Online trajectory classification
ICCS'03 Proceedings of the 2003 international conference on Computational science: PartIII
Beyond prototyping in the factory of agents
CEEMAS'03 Proceedings of the 3rd Central and Eastern European conference on Multi-agent systems
ENTER: the personalisation and contextualisation of 3-dimensional worlds
EURO-PDP'00 Proceedings of the 8th Euromicro conference on Parallel and distributed processing
Developing effective navigation techniques in virtual 3D environments
EG VE'00 Proceedings of the 6th Eurographics conference on Virtual Environments
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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.