Interaction modeling and prediction in smart spaces: a bio-inspired approach based on autobiographical memory

Authors:
Alessio Dore;Andrea Fabio Cattoni;Carlo S. Regazzoni
Affiliations:
Institute of Biomedical Engineering, Imperial College London, London, UK;Department of Electronic Systems, Aalborg University, Aalborg and Nokia Siemens Network, Aalborg, Denmark;Department of Biophysical and Electronic Engineering, University of Genoa, Genova, Italy
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Year:
2010

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Abstract

In Smart Spaces (SSs), the capability of learning from experience is fundamental for autonomous adaptation to environmental changes and for proactive interaction with users. New research trends for reaching this goal are based on neurophysiological observations of human brain structure and functioning. A learning technique that is used to provide the SS with the so-called Autobiographical Memory is presented here by drawing inspiration froma bio-inspired model of the interactions occurring between the system and the user. Starting from the hypothesis that user's actions have a direct influence on the internal system state variables and vice versa, a statistical voting algorithm is proposed for inferring the cause/effect relationships among users and the system. The main contribution of this paper lies in proposing a general framework that is able to allow the SS to be aware of its present state as well as of the behavior of its users and to be able to predict the expected consequences of user actions.