Fuzzy Ensembles for Embedding Adaptive Behaviours in Semi-Autonomous Avatars in 3D Virtual Worlds

Book chapter

Wall, J., Izquierdo, E. and Zhang, Q. 2013. Fuzzy Ensembles for Embedding Adaptive Behaviours in Semi-Autonomous Avatars in 3D Virtual Worlds. in: Proceedings 2013 18th International Conference on Digital Signal Processing (DSP) IEEE. pp. 1-6
AuthorsWall, J., Izquierdo, E. and Zhang, Q.
Abstract

Semi-autonomous avatars should be both realistic and believable. The goal is to learn from and reproduce the behaviours of the user-controlled input to enable semi-autonomous avatars to plausibly interact with their human-controlled counterparts. A powerful tool for embedding autonomous behaviour is learning by imitation. Hence, in this paper an ensemble of fuzzy inference systems cluster the user input data to identify natural groupings within the data to describe the users movement and actions in a more abstract way. Multiple clustering algorithms are investigated along with a neuro-fuzzy classifier; and an ensemble of fuzzy systems are evaluated.

Keywordsfuzzy ensembles; fuzzy inference system; semi-autonomous avatar; virtual environments
Book titleProceedings 2013 18th International Conference on Digital Signal Processing (DSP)
Page range1-6
Year2013
PublisherIEEE
Publication dates
PrintJul 2013
Publication process dates
Deposited03 Dec 2015
Event2013 18th International Conference on Digital Signal Processing (DSP)
ISBN978-1-4673-5805-7
Digital Object Identifier (DOI)https://doi.org/10.1109/ICDSP.2013.6622818
FunderEuropean Commission
Web address (URL)http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6622818&tag=1
https://www.researchgate.net/publication/256437200_Fuzzy_Ensembles_for_Embedding_Adaptive_Behaviours_in_Semi-Autonomous_Avatars_in_3D_Virtual_Worlds
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© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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