An ambient intelligence approach for learning in smart robotic environments

Davide Bacciu, Maurizio Di Rocco, Mauro Dragone, Claudio Gallicchio, Alessio Micheli, Alessandro Saffiotti

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
Pages (from-to)1061-1088
Number of pages28
JournalComputational Intelligence
Volume35
Issue number4
Early online date31 Jul 2019
DOIs
Publication statusPublished - Nov 2019

Keywords

  • adaptive planning
  • ambient intelligence
  • recurrent neural networks
  • robotic ecology
  • self-adaptive system
  • smart environment

ASJC Scopus subject areas

  • Computational Mathematics
  • Artificial Intelligence

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  • Cite this

    Bacciu, D., Di Rocco, M., Dragone, M., Gallicchio, C., Micheli, A., & Saffiotti, A. (2019). An ambient intelligence approach for learning in smart robotic environments. Computational Intelligence, 35(4), 1061-1088. https://doi.org/10.1111/coin.12233