@article{rubicon2019CI,

title = {An Ambient Intelligence Approach for Learning in Smart Robotic Environments},

author = {Bacciu Davide and Di Rocco Maurizio and Dragone Mauro and Gallicchio Claudio and Micheli Alessio and Saffiotti Alessandro},

doi = {10.1111/coin.12233},

year  = {2019},

date = {2019-07-31},

journal = {Computational Intelligence},

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. In order to reduce the amount of preparation and pre-programming required for their deployment in real world applications,  it is important to make these systems self-learning, self-configuring, and 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 pro-actively and smoothly adapt to subtle changes in the environment and in the habits and preferences

of their user(s).},

note = {Early View (Online Version of Record before inclusion in an issue)

},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Dragone:2015:CRE:2827370.2827596,

title = {A Cognitive Robotic Ecology Approach to Self-configuring and Evolving AAL Systems},

author = {Dragone Mauro and Amato Giuseppe and Bacciu Davide and Chessa Stefano and Coleman Sonya and Di Rocco Maurizio and Gallicchio Claudio and Gennaro Claudio and Lozano Hector and Maguire Liam and McGinnity Martin and Micheli Alessio and O'Hare Gregory M.P. and Renteria Arantxa and Saffiotti Alessandro and Vairo Claudio and Vance Philip},

url = {http://dx.doi.org/10.1016/j.engappai.2015.07.004},

doi = {10.1016/j.engappai.2015.07.004},

issn = {0952-1976},

year  = {2015},

date = {2015-01-01},

urldate = {2015-01-01},

journal = {Engineering Applications of Artificial Intelligence},

volume = {45},

number = {C},

pages = {269--280},

publisher = {Pergamon Press, Inc.},

address = {Tarrytown, NY, USA},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{bacciuJirs15,

title = {Robotic Ubiquitous Cognitive Ecology for Smart Homes},

author = {Amato Giuseppe and Bacciu Davide and Broxvall Mathias and Chessa Stefano and Coleman Sonya and Di Rocco Maurizio and Dragone Mauro and Gallicchio Claudio and Gennaro Claudio and Lozano Hector and McGinnity T Martin and Micheli Alessio and Ray AK and Renteria Arantxa and Saffiotti Alessandro and Swords David and Vairo Claudio and Vance Philip},

url = {http://dx.doi.org/10.1007/s10846-015-0178-2},

doi = {10.1007/s10846-015-0178-2},

issn = {0921-0296},

year  = {2015},

date = {2015-01-01},

journal = {Journal of Intelligent & Robotic Systems},

volume = {80},

number = {1},

pages = {57-81},

publisher = {Springer Netherlands},

abstract = {Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent-based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a proof of concept smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feedback received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}