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An ambient intelligence approach for learning in smart robotic environments
Author(s) -
Bacciu Davide,
Di Rocco Maurizio,
Dragone Mauro,
Gallicchio Claudio,
Micheli Alessio,
Saffiotti Alessandro
Publication year - 2019
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/coin.12233
Subject(s) - ambient intelligence , computer science , software deployment , adaptation (eye) , human–computer interaction , smart environment , robot , mobile device , artificial intelligence , embedded system , internet of things , physics , optics , operating system
Smart robotic environments combine traditional (ambient) sensing devices and mobile robots. This combination extends the type of applications that can be considered, reduces their complexity, and enhances the individual values of the devices involved by enabling new services that cannot be performed by a single device. To reduce the amount of preparation and preprogramming required for their deployment in real‐world applications, it is important to make these systems self‐adapting. The solution presented in this paper is based upon a type of compositional adaptation where (possibly multiple) plans of actions are created through planning and involve the activation of pre‐existing capabilities. All the devices in the smart environment participate in a pervasive learning infrastructure, which is exploited to recognize which plans of actions are most suited to the current situation. The system is evaluated in experiments run in a real domestic environment, showing its ability to proactively and smoothly adapt to subtle changes in the environment and in the habits and preferences of their user(s), in presence of appropriately defined performance measuring functions.