Here you can find a consolidated (a.k.a. slowly updated) list of my publications. A frequently updated (and possibly noisy) list of works is available on my Google Scholar profile.
Please find below a short list of highlight publications for my recent activity.
Davide, Bacciu; Maurizio, Di Rocco; Mauro, Dragone; Claudio, Gallicchio; Alessio, Micheli; Alessandro, Saffiotti An Ambient Intelligence Approach for Learning in Smart Robotic Environments Journal Article In: Computational Intelligence, 2019, (Early View (Online Version of Record before inclusion in an issue)
). Giuseppe, Amato; Davide, Bacciu; Stefano, Chessa; Mauro, Dragone; Claudio, Gallicchio; Claudio, Gennaro; Hector, Lozano; Alessio, Micheli; Arantxa, Renteria; Claudio, Vairo A Benchmark Dataset for Human Activity Recognition and Ambient Assisted Living Conference Proceedings of the 7th International Conference on Ambient Intelligence (ISAMI'16), vol. 476, Advances in Intelligent Systems and Computing Springer, 2016, ISBN: 978-3-319-40113-3. Mauro, Dragone; Giuseppe, Amato; Davide, Bacciu; Stefano, Chessa; Sonya, Coleman; Maurizio, Di Rocco; Claudio, Gallicchio; Claudio, Gennaro; Hector, Lozano; Liam, Maguire; Martin, McGinnity; Alessio, Micheli; M.P., O'Hare Gregory; Arantxa, Renteria; Alessandro, Saffiotti; Claudio, Vairo; Philip, Vance A Cognitive Robotic Ecology Approach to Self-configuring and Evolving AAL Systems Journal Article In: Engineering Applications of Artificial Intelligence, vol. 45, no. C, pp. 269–280, 2015, ISSN: 0952-1976. Giuseppe, Amato; Davide, Bacciu; Mathias, Broxvall; Stefano, Chessa; Sonya, Coleman; Maurizio, Di Rocco; Mauro, Dragone; Claudio, Gallicchio; Claudio, Gennaro; Hector, Lozano; Martin, McGinnity T; Alessio, Micheli; AK, Ray; Arantxa, Renteria; Alessandro, Saffiotti; David, Swords; Claudio, Vairo; Philip, Vance Robotic Ubiquitous Cognitive Ecology for Smart Homes Journal Article In: Journal of Intelligent & Robotic Systems, vol. 80, no. 1, pp. 57-81, 2015, ISSN: 0921-0296. Davide, Bacciu; Claudio, Gallicchio; Alessio, Micheli; Maurizio, Di Rocco; Alessandro, Saffiotti Learning context-aware mobile robot navigation in home environments Conference Proceedings of the 5th International Conference on Information, Intelligence, Systems and Applications (IISA 2014), IEEE, 2014, ISBN: 9781479961702. Davide, Bacciu; Stefano, Chessa; Claudio, Gallicchio; Alessandro, Lenzi; Alessio, Micheli; Susanna, Pelagatti A General Purpose Distributed Learning Model for Robotic Ecologies Conference Robot Control - 10th IFAC Symposium on Robot Control, vol. 10, ELSEVIER SCIENCE BV, 2012. Same, Abdel-Naby; Giuseppe, Amato; Davide, Bacciu; Mathias, Broxvall; Stefano, Chessa; Sonya, Coleman; Maurizio, Di Rocco; Mauro, Dragone; Claudio, Gallicchio; Claudio, Gennaro; Roberto, Guzman; Raul, Lopez; Hector, Lozano; Liam, Maguire; Martin, McGinnity T; Alessio, Micheli; MP, O'Hare Greg; Federico, Pecora; AK, Ray; Arantxa, Renteria; Alessandro, Saffiotti; David, Swords; Claudio, Vairo Robotic UBIquitous COgnitive Networks Presentation 01.01.2012. Davide, BACCIU; Mathias, Broxvall; Sonya, Coleman; Mauro, Dragone; Claudio, Gallicchio; Claudio, Gennaro; Roberto, Guzman; Raul, Lopez; Hector, Lozano-Peiteado; AK, Ray; Arantxa, Renteria; Alessandro, Saffiotti; Claudio, Vairo Self-Sustaining Learning for Robotic Ecologies Conference Proceedings of the 1st International Conference on Sensor Networks, SENSORNETS 2012, 2012.@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}
}
of their user(s).@conference{Amato2016,
title = {A Benchmark Dataset for Human Activity Recognition and Ambient Assisted Living},
author = {Amato Giuseppe and Bacciu Davide and Chessa Stefano and Dragone Mauro and Gallicchio Claudio and Gennaro Claudio and Lozano Hector and Micheli Alessio and Renteria Arantxa
and Vairo Claudio},
doi = {10.1007/978-3-319-40114-0_1},
isbn = {978-3-319-40113-3},
year = {2016},
date = {2016-06-03},
booktitle = {Proceedings of the 7th International Conference on Ambient Intelligence (ISAMI'16)},
volume = {476},
pages = {1-9},
publisher = {Springer},
series = {Advances in Intelligent Systems and Computing},
abstract = {We present a data benchmark for the assessment of human activity recognition solutions, collected as part of the EU FP7 RUBICON project, and available to the scientific community. The dataset provides fully annotated data pertaining to numerous user activities and comprises synchronized data streams collected from a highly sensor-rich home environment. A baseline activity recognition performance obtained through an Echo State Network approach is provided along with the dataset.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@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}
}
@conference{11568_588269,
title = {Learning context-aware mobile robot navigation in home environments},
author = {Bacciu Davide and Gallicchio Claudio and Micheli Alessio and Di Rocco Maurizio and Saffiotti Alessandro},
doi = {10.1109/IISA.2014.6878733},
isbn = {9781479961702},
year = {2014},
date = {2014-01-01},
booktitle = {Proceedings of the 5th International Conference on Information, Intelligence, Systems and Applications (IISA 2014)},
pages = {57--62},
publisher = {IEEE},
abstract = {We present an approach to make planning adaptive in order to enable context-aware mobile robot navigation. We integrate a model-based planner with a distributed learning system based on reservoir computing, to yield personalized planning and resource allocations that account for user preferences and environmental changes. We demonstrate our approach in a real robot ecology, and show that the learning system can effectively exploit historical data about navigation performance to modify the models in the planner, without any prior information oncerning the phenomenon being modeled. The plans produced by the adapted CL fail more rarely than the ones generated by a non-adaptive planner. The distributed learning system handles the new learning task autonomously, and is able to automatically identify the sensorial information most relevant for the task, thus reducing the communication and computational overhead of the predictive task},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@conference{11568_193770,
title = {A General Purpose Distributed Learning Model for Robotic Ecologies},
author = {Bacciu Davide and Chessa Stefano and Gallicchio Claudio and Lenzi Alessandro and Micheli Alessio and Pelagatti Susanna},
url = {http://www.ifac-papersonline.net/Detailed/55807.html},
doi = {10.3182/20120905-3-HR-2030.00178},
year = {2012},
date = {2012-01-01},
booktitle = {Robot Control - 10th IFAC Symposium on Robot Control},
journal = {IFAC PROCEEDINGS VOLUMES},
volume = {10},
pages = {435--440},
publisher = {ELSEVIER SCIENCE BV},
abstract = {The design of a learning system for robotic ecologies need to account for some key aspects of the ecology model such as distributivity, heterogeneity of the computational, sensory and actuator capabilities, as well as self-configurability. The paper proposes general guiding principles for learning systems' design that ensue from key ecology properties, and presents a distributed learning system for the Rubicon ecology that draws inspiration from such guidelines. The proposed learning system provides the Rubicon ecology with a set of general-purpose learning services which can be used to learn generic computational tasks that involve predicting information of interest based on dynamic sensorial input streams.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@misc{11568_466873,
title = {Robotic UBIquitous COgnitive Networks},
author = {Abdel-Naby Same and 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 Guzman Roberto and Lopez Raul and Lozano Hector and Maguire Liam and McGinnity T Martin and Micheli Alessio and O'Hare Greg MP and Pecora Federico and Ray AK and Renteria Arantxa and Saffiotti Alessandro and Swords David and Vairo Claudio},
year = {2012},
date = {2012-01-01},
booktitle = {Poster in the 5th International Conference on Cognitive Systems (CogSys 2012)},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
@conference{11568_466867,
title = {Self-Sustaining Learning for Robotic Ecologies},
author = {BACCIU Davide and Broxvall Mathias and Coleman Sonya and Dragone Mauro and Gallicchio Claudio and Gennaro Claudio and Guzman Roberto and Lopez Raul and Lozano-Peiteado Hector and Ray AK and Renteria Arantxa and Saffiotti Alessandro and Vairo Claudio},
year = {2012},
date = {2012-01-01},
booktitle = {Proceedings of the 1st International Conference on Sensor Networks, SENSORNETS 2012},
pages = {99--103},
abstract = {The most common use of wireless sensor networks (WSNs) is to collect environmental data from a specific area, and to channel it to a central processing node for on-line or off-line analysis. The WSN technology, however, can be used for much more ambitious goals. We claim that merging the concepts and technology of WSN with the concepts and technology of distributed robotics and multi-agent systems can open new ways to design systems able to provide intelligent services in our homes and working places. We also claim that endowing these systems with learning capabilities can greatly increase their viability and acceptability, by simplifying design, customization and adaptation to changing user needs. To support these claims, we illustrate our architecture for an adaptive robotic ecology, named RUBICON, consisting of a network of sensors, effectors and mobile robots.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}