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

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.

Feasibility of Undertaking Off-Site Infant Eye-Tracking Assessments of Neuro-Cognitive Functioning in Early-Intervention Centres

Recent work suggests that differences in functional brain development are already identifiable in 6- to 9-month-old infants from low socio-economic status (SES) backgrounds. Investigation of early SES-related differences in neuro-cognitive functioning requires the recruitment of large and diverse samples of infants, yet it is often difficult to persuade low-SES parents to come to a university setting. One solution is to recruit infants through early intervention children’s centres (CCs). These are often located in areas of high relative deprivation to support young children. Given the increasing portability of eye-tracking equipment, assessment of large clusters of infants could be undertaken in centres by suitably trained early intervention staff. Here, we report on a study involving 174 infants and their parents, carried out in partnership with CCs, exploring the feasibility of this approach. We report the processes of setting up the project and participant recruitment. We report the diversity of sample obtained on the engagement of CC staff in training and the process of assessment itself.We report the quality of the data obtained, and the levels of engagement of parents and infants. We conclude that this approach has great potential for recruiting large and diverse samples worldwide, provides sufficiently reliable data and is engaging to staff, parents and infants.