A cognitive robotic ecology approach to self-configuring and evolving AAL systems

Robotic ecologies are systems made out of several robotic devices, including mobile robots, wireless sensors and effectors embedded in everyday environments, where they cooperate to achieve complex tasks. This paper demonstrates how endowing robotic ecologies with information processing algorithms such as perception, learning, planning, and novelty detection can make these systems able to deliver modular, flexible, manageable and dependable Ambient Assisted Living (AAL) solutions. Specifically, we show how the integrated and self-organising cognitive solutions implemented within the EU project RUBICON (Robotic UBIquitous Cognitive Network) can reduce the need of costly pre-programming and maintenance of robotic ecologies. We illustrate how these solutions can be harnessed to (i) deliver a range of assistive services by coordinating the sensing & acting capabilities of heterogeneous devices, (ii) adapt and tune the overall behaviour of the ecology to the preferences and behaviour of its inhabitants, and also (iii) deal with novel events, due to the occurrence of new user's activities and changing user's habits.

[1]  Lotfi A. Zadeh,et al.  The Concepts of a Linguistic Variable and its Application to Approximate Reasoning , 1975 .

[2]  Claudio Gallicchio,et al.  Architectural and Markovian factors of echo state networks , 2011, Neural Networks.

[3]  Alessio Micheli,et al.  Robotic UBIquitous COgnitive Network , 2012, ISAmI.

[4]  Stefano Chessa,et al.  MaD-WiSe: a distributed stream management system for wireless sensor networks , 2010 .

[5]  T. Martin McGinnity,et al.  Development of Cognitive Capabilities for Smart Home Using a Self-Organizing Fuzzy Neural Network , 2012, SyRoCo.

[6]  T. Martin McGinnity,et al.  An on-line algorithm for creating self-organizing fuzzy neural networks , 2004, Neural Networks.

[7]  Gerhard Widmer,et al.  Learning in the presence of concept drift and hidden contexts , 2004, Machine Learning.

[8]  Alessandro Saffiotti,et al.  Autonomous functional configuration of a network robot system , 2008, Robotics Auton. Syst..

[9]  Diane J. Cook,et al.  CASAS: A Smart Home in a Box , 2013, Computer.

[10]  Alessandro Saffiotti,et al.  Plan-Based Configuration of an Ecology of Robots , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[11]  Bernt Schiele,et al.  Discovery of activity patterns using topic models , 2008 .

[12]  Alessandro Saffiotti,et al.  Learning context-aware mobile robot navigation in home environments , 2014, IISA 2014, The 5th International Conference on Information, Intelligence, Systems and Applications.

[13]  Paolo Barsocchi,et al.  Evaluating AAL Systems Through Competitive Benchmarking , 2013, Communications in Computer and Information Science.

[14]  Stefano Chessa,et al.  A General Purpose Distributed Learning Model for Robotic Ecologies , 2012, SyRoCo.

[15]  Stefano Chessa,et al.  When Wireless Sensor Networks Meet Robots , 2012, ICSNC 2012.

[16]  James F. Allen.  Towards a General Theory of Action and Time , 1984, Artif. Intell..

[17]  Diane J. Cook,et al.  Learning frequent behaviours of the users in Intelligent Environments , 2010, J. Ambient Intell. Smart Environ..

[18]  Juan Carlos Niebles,et al.  Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words , 2008, International Journal of Computer Vision.

[19]  Paul Lukowicz,et al.  Activity Recognition in Opportunistic Sensor Environments , 2011, FET.

[20]  Gérard Bailly,et al.  Proceedings of the 2005 joint conference on Smart objects and ambient intelligence - innovative context-aware services: usages and technologies, sOc-EUSAI '05, Grenoble, France, October 12-14, 2005 , 2005, sOc-EUSAI.

[21]  Charles R. Severance.  Mitchell Baker: The Mozilla Foundation , 2013, Computer.

[22]  Diane J. Cook,et al.  Detecting Anomalous Sensor Events in Smart Home Data for Enhancing the Living Experience , 2011, Artificial Intelligence and Smarter Living.

[23]  Alessandro Saffiotti,et al.  Affordances in an Ecology of Physically Embedded Intelligent Systems , 2006, Towards Affordance-Based Robot Control.

[24]  Diane J. Cook,et al.  Keeping the Resident in the Loop: Adapting the Smart Home to the User , 2009, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[25]  Diane J. Cook,et al.  Activity Discovery and Activity Recognition: A New Partnership , 2013, IEEE Transactions on Cybernetics.

[26]  S. Marsland.  Novelty Detection in Learning Systems , 2008 .

[27]  Diane J. Cook,et al.  Discovering frequent user--environment interactions in intelligent environments , 2011, Personal and Ubiquitous Computing.

[28]  Diane J. Cook,et al.  Mining the home environment , 2014, Journal of Intelligent Information Systems.

[29]  Lovekesh Vig,et al.  Multi-robot coalition formation , 2006, IEEE Transactions on Robotics.

[30]  Harald Haas,et al.  Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication , 2004, Science.

[31]  Gregory M. P. O'Hare,et al.  Dynamic sensor event segmentation for real-time activity recognition in a smart home context , 2014, Personal and Ubiquitous Computing.

[32]  Kent Larson,et al.  Activity Recognition in the Home Using Simple and Ubiquitous Sensors , 2004, Pervasive.

[33]  Kerstin Dautenhahn,et al.  A user friendly robot architecture for re-ablement and co-learning in a sensorised home , 2013 .

[34]  Alessandro Saffiotti,et al.  Configuration Planning with Multiple Dynamic Goals , 2013, AAAI Spring Symposium: Designing Intelligent Robots.

[35]  Sonya Coleman,et al.  Online Sliding Window Based Self-Organising Fuzzy Neural Network for Cognitive Reasoning , 2013 .

[36]  Amedeo Cesta,et al.  Unifying planning and scheduling as timelines in a component-based perspective , 2008 .

[37]  Vipin Kumar,et al.  Anomaly Detection for Discrete Sequences: A Survey , 2012, IEEE Transactions on Knowledge and Data Engineering.

[38]  Lynne E. Parker,et al.  ASyMTRe: Automated Synthesis of Multi-Robot Task Solutions through Software Reconfiguration , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[39]  Paul Lukowicz,et al.  A Framework for Opportunistic Activity and Context Recognition , 2010 .

[40]  Gregory M. P. O'Hare,et al.  A Programming Framework for Multi-agent Coordination of Robotic Ecologies , 2012, ProMAS.

[41]  Mamun Bin Ibne Reaz,et al.  SPEED: An Inhabitant Activity Prediction Algorithm for Smart Homes , 2012, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[42]  Ahmad Lotfi,et al.  Smart homes for the elderly dementia sufferers: identification and prediction of abnormal behaviour , 2012, J. Ambient Intell. Humaniz. Comput..

[43]  Amedeo Cesta,et al.  GiraffPlus: Combining social interaction and long term monitoring for promoting independent living , 2013, 2013 6th International Conference on Human System Interactions (HSI).

[44]  Simon A. Dobson,et al.  Situation identification techniques in pervasive computing: A review , 2012, Pervasive Mob. Comput..

[45]  Stefano Chessa,et al.  Multisensor Data Fusion for Activity Recognition Based on Reservoir Computing , 2013, EvAAL.

[46]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[47]  Alessandro Saffiotti,et al.  PEIS ecologies: ambient intelligence meets autonomous robotics , 2005, sOc-EUSAI '05.

[48]  Davide Bacciu,et al.  An experimental characterization of reservoir computing in ambient assisted living applications , 2013, Neural Computing and Applications.

[49]  Mathias Broxvall,et al.  The PEIS kernel : a middleware for ubiquitous robotics , 2007 .

[50]  Ahmad Lotfi,et al.  Activities Recognition in Intelligent Office Environment , 2013, Intelligent Environments.

[51]  Juan Carlos Niebles,et al.  Unsupervised Learning of Human Action Categories , 2006 .

[52]  Sameer Singh,et al.  Novelty detection: a review - part 2: : neural network based approaches , 2003, Signal Process..

[53]  Lotfi A. Zadeh,et al.  The concept of a linguistic variable and its application to approximate reasoning-III , 1975, Inf. Sci..

[54]  T. Martin McGinnity,et al.  Novelty detection in user behavioural models within ambient assisted living applications: An experimental evaluation , 2014, 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014).

[55]  Lawrence B. Holder,et al.  Discovering Activities to Recognize and Track in a Smart Environment , 2011, IEEE Transactions on Knowledge and Data Engineering.

[56]  Victoria J. Hodge,et al.  A Survey of Outlier Detection Methodologies , 2004, Artificial Intelligence Review.