Using Fuzzy Logic to Monitor the State of an Ubiquitous Robotic System

A trend is emerging in the fields of ambient intelligence (AmI) and autonomous robotics, which points in the direction of a merger between these two fields. The inclusion of robotic devices in AmI system, sometimes named ubiquitous robotics, makes one of the hard problems in this field even harder: how can we provide a comfortable, natural interface between the everyday user and a complex system which consists of a large multitude of highly heterogeneous devices? In this paper, we address a specific, important aspect of this problem: to enable the user of an ubiquitous robotic system to monitor the state of this system in a natural way. The solution that we propose is based on two mechanisms: an expression-based semantics to represent in a uniform way the status of heterogeneous devices; and a common interface point to aggregate the information from all devices into a summary status presented to the user. For both mechanisms, we propose to use the tools of fuzzy logic. We justify this choice by arguments grounded in the semantics and formal properties of fuzzy logic. We also illustrate our approach on a specific type of ubiquitous robotic system called Ecology of Physically Embedded Intelligent Systems, or PEIS-Ecology.

[1]  George J. Klir,et al.  Fuzzy sets, uncertainty and information , 1988 .

[2]  Simon Parsons,et al.  Qualitative methods for reasoning under uncertainty , 2001 .

[3]  Ueli Rutishauser,et al.  Control and learning of ambience by an intelligent building , 2005, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[4]  Jean Scholtz,et al.  Theory and evaluation of human robot interactions , 2003, 36th Annual Hawaii International Conference on System Sciences, 2003. Proceedings of the.

[5]  Gregg C. Vanderheiden,et al.  Universal remote console standard: toward natural user interaction in ambient intelligence , 2004, CHI EA '04.

[6]  Enrique H. Ruspini,et al.  On the semantics of fuzzy logic , 1991, Int. J. Approx. Reason..

[7]  D. Norman Emotional design : why we love (or hate) everyday things , 2004 .

[8]  Kai-Florian Richter,et al.  Interacting with the Ambience : Multimodal Interaction and Ambient Intelligence Position Paper to the W 3 C Workshop on Multimodal Interaction , 19-20 July 2004 , 2004 .

[9]  W. Keith Edwards,et al.  At Home with Ubiquitous Computing: Seven Challenges , 2001, UbiComp.

[10]  Alessandro Saffiotti,et al.  Seamless integration of robots and tiny embedded devices in a PEIS-Ecology , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  Giovanni Acampora,et al.  Using Fuzzy Technology in Ambient Intelligence Environments , 2005, The 14th IEEE International Conference on Fuzzy Systems, 2005. FUZZ '05..

[12]  Boris E. R. de Ruyter,et al.  Assessing the effects of building social intelligence in a robotic interface for the home , 2005, Interact. Comput..

[13]  Joo-Ho Lee,et al.  Intelligent Space — concept and contents , 2002, Adv. Robotics.

[14]  Giuseppe Riva,et al.  Ambient Intelligence in Health Care , 2003, Cyberpsychology Behav. Soc. Netw..

[15]  Constantine Stephanidis,et al.  Universal access to ambient intelligence environments: Opportunities and challenges for people with disabilities , 2005, IBM Syst. J..

[16]  Giovanni Acampora,et al.  Using FML and fuzzy technology in adaptive ambient intelligent environments , 2005 .

[17]  Emile H. L. Aarts,et al.  Ambient intelligence: a multimedia perspective , 2004, IEEE MultiMedia.

[18]  R. Hursthouse THE LOGIC OF DECISION AND ACTION , 1969 .

[19]  T. Furuhashi,et al.  A study on fuzzy abductive inference , 1995, Proceedings of 1995 IEEE International Conference on Fuzzy Systems..

[20]  Michael Hellenschmidt,et al.  An integrated user interface providing unified access to intelligent environments and personal media , 2004, EUSAI '04.

[21]  Mai Gehrke,et al.  Normal forms and truth tables for fuzzy logics , 2003, Fuzzy Sets Syst..

[22]  Anthony Willing A note on Rescher's ‘Semantic Foundations for the Logic of Preference’ , 1976 .

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

[24]  P. Valdez,et al.  Effects of color on emotions. , 1994, Journal of experimental psychology. General.

[25]  Wonjun Lee,et al.  Universal interactions with smart spaces , 2006, IEEE Pervasive Computing.

[26]  Alessandro Saffiotti,et al.  PEIS Ecology: integrating robots into smart environments , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[27]  Hani Hagras,et al.  An Incremental Adaptive Life Long Learning Approach for Type-2 Fuzzy Embedded Agents in Ambient Intelligent Environments , 2007, IEEE Transactions on Fuzzy Systems.

[28]  S. Weber A general concept of fuzzy connectives, negations and implications based on t-norms and t-conorms , 1983 .

[29]  Luigi Fortuna,et al.  Soft computing for greenhouse climate control , 2000, IEEE Trans. Fuzzy Syst..

[30]  Enrique H. Ruspini,et al.  Truth as Utility: A Conceptual Synthesis , 1991, UAI.

[31]  Jong-Hwan Kim The 3 rd Generation of Robotics: Ubiquitous Robot. , 2005 .

[32]  David Gelernter,et al.  Generative communication in Linda , 1985, TOPL.

[33]  Jong-Hwan Kim,et al.  The Third Generation of Robotics : Ubiquitous Robot , 2004 .

[34]  Hani Hagras,et al.  A fuzzy embedded agent-based approach for realizing ambient intelligence in intelligent inhabited environments , 2005, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.