A multi-agent fuzzy consensus model in a Situation Awareness framework

Graphical abstractDisplay Omitted In order to define systems enabling the automatic identification of occurring situations, numerous approaches employing intelligent software agents to analyse data coming from deployed sensors have been proposed. Thus, it is possible that more agents are committed to monitor the same phenomenon in the same environment. Redundancy of sensors and agents is needed, for instance, in real world applications in order to mitigate the risk of faults and threats. One of the possible side effects produced by redundancy is that agents, observing the same phenomenon, could provide discordant opinions. Indeed, solid mechanisms for reaching an agreement among these agents and produce a shared consensus on the same observations are needed. This paper proposes an approach to integrate a fuzzy-based consensus model into a Situation Awareness framework. The main idea is to consider intelligent agents as experts claiming their opinions (preferences) on a phenomenon of interest.

[1]  Arkady B. Zaslavsky,et al.  Semantic-Driven Configuration of Internet of Things Middleware , 2013, 2013 Ninth International Conference on Semantics, Knowledge and Grids.

[2]  Matteo Gaeta,et al.  Resilient Semantic Sensor Middleware , 2014, IDC.

[3]  Asunción Gómez-Pérez,et al.  A Context Ontology for Mobile Environments , 2010, CIAO@EKAW.

[4]  Francisco Herrera,et al.  Direct approach processes in group decision making using linguistic OWA operators , 1996, Fuzzy Sets Syst..

[5]  Alok Aggarwal,et al.  Situation recognition in sensor based environments using concept lattices , 2012, CUBE.

[6]  Witold Pedrycz,et al.  Building consensus in group decision making with an allocation of information granularity , 2014, Fuzzy Sets Syst..

[7]  Bart Kosko,et al.  Fuzzy Cognitive Maps , 1986, Int. J. Man Mach. Stud..

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

[9]  Enrique Herrera-Viedma,et al.  A New Consensus Model for Group Decision Making Problems With Non-Homogeneous Experts , 2014, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[10]  Enrique Herrera-Viedma,et al.  Analyzing consensus approaches in fuzzy group decision making: advantages and drawbacks , 2010, Soft Comput..

[11]  Rashaad E. T. Jones,et al.  Modeling Situation Awareness for Army Infantry Platoon Leaders Using Fuzzy Cognitive Mapping Techniques , 2010 .

[12]  David I. Gertman,et al.  Resilient control systems: Next generation design research , 2009, 2009 2nd Conference on Human System Interactions.

[13]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[14]  Thomas R. Gruber,et al.  Toward principles for the design of ontologies used for knowledge sharing? , 1995, Int. J. Hum. Comput. Stud..

[15]  Kay Römer,et al.  Fuzzy-based sensor search in the Web of Things , 2012, 2012 3rd IEEE International Conference on the Internet of Things.

[16]  P. Barnaghi,et al.  Computing perception from sensor data , 2012, 2012 IEEE Sensors.

[17]  Enrique Herrera-Viedma,et al.  A Mobile Decision Support System for Dynamic Group Decision-Making Problems , 2010, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[18]  Arkady B. Zaslavsky,et al.  CA4IOT: Context Awareness for Internet of Things , 2012, 2012 IEEE International Conference on Green Computing and Communications.

[19]  Karl Aberer,et al.  Global Sensor Networks , 2006 .

[20]  Bernhard Ganter,et al.  Formal Concept Analysis: Mathematical Foundations , 1998 .

[21]  Wolfgang Kellerer,et al.  Sensor ranking: A primitive for efficient content-based sensor search , 2009, 2009 International Conference on Information Processing in Sensor Networks.

[22]  E. Herrera-Viedma,et al.  A new consensus model for group decision making using fuzzy ontology , 2013, Soft Comput..

[23]  Rainer Palm,et al.  Perception modeling for human-like artificial sensor systems , 2007, Int. J. Hum. Comput. Stud..

[24]  Francesco Orciuoli,et al.  Towards Perception-Oriented Situation Awareness Systems , 2014, IEEE Conf. on Intelligent Systems.

[25]  Tzung-Pei Hong,et al.  An Overview of Mining Fuzzy Association Rules , 2008, Fuzzy Sets and Their Extensions: Representation, Aggregation and Models.

[26]  Arkady B. Zaslavsky,et al.  Context-Aware Sensor Search, Selection and Ranking Model for Internet of Things Middleware , 2013, 2013 IEEE 14th International Conference on Mobile Data Management.

[27]  R. Doyle.  The American terrorist. , 2001, Scientific American.

[28]  Antonio Iera,et al.  The Internet of Things: A survey , 2010, Comput. Networks.

[29]  Mingzhu Zhang,et al.  Survey on Association Rules Mining Algorithms , 2010 .

[30]  Matteo Gaeta,et al.  Sensor Deployment for Network-Like Environments , 2010, IEEE Transactions on Automatic Control.

[31]  Jadwiga Indulska,et al.  A survey of context modelling and reasoning techniques , 2010, Pervasive Mob. Comput..

[32]  Siu Cheung Hui,et al.  Automatic fuzzy ontology generation for semantic Web , 2006, IEEE Transactions on Knowledge and Data Engineering.

[33]  Enrique Herrera-Viedma,et al.  A linguistic consensus model for Web 2.0 communities , 2013, Appl. Soft Comput..

[34]  Mica R. Endsley,et al.  Toward a Theory of Situation Awareness in Dynamic Systems , 1995, Hum. Factors.

[35]  Kenneth Baclawski,et al.  A core ontology for situation awareness , 2003, Sixth International Conference of Information Fusion, 2003. Proceedings of the.

[36]  Sotiris B. Kotsiantis,et al.  Machine learning: a review of classification and combining techniques , 2006, Artificial Intelligence Review.

[37]  Mieczyslaw M. Kokar,et al.  Situation Awareness and Cognitive Modeling , 2012, IEEE Intelligent Systems.

[38]  Karl Aberer,et al.  Infrastructure for Data Processing in Large-Scale Interconnected Sensor Networks , 2007, 2007 International Conference on Mobile Data Management.

[39]  Enrique Herrera-Viedma,et al.  A consensus model for group decision making problems with linguistic interval fuzzy preference relations , 2012, Expert Syst. Appl..

[40]  Simon A. Dobson,et al.  Using Dempster-Shafer Theory of Evidence for Situation Inference , 2009, EuroSSC.

[41]  Salvatore Venticinque,et al.  Intelligent Distributed Computing VIII - Proceedings of the 8th International Symposium on Intelligent Distributed Computing, IDC 2014, Madrid, Spain, September 3-5, 2014 , 2015, IDC.

[42]  Peter P. Groumpos,et al.  Fuzzy Cognitive Maps: Basic Theories and Their Application to Complex Systems , 2010 .

[43]  Werner Retschitzegger,et al.  A software architecture for ontology-driven situation awareness , 2008, SAC '08.

[44]  Werner Retschitzegger,et al.  BeAware! - Situation awareness, the ontology-driven way , 2010, Data Knowl. Eng..

[45]  Witold Pedrycz,et al.  A review of soft consensus models in a fuzzy environment , 2014, Inf. Fusion.

[46]  Enrique Herrera-Viedma,et al.  Reaching Consensus in Digital Libraries: A Linguistic Approach , 2014, ITQM.

[47]  Vincenzo Loia,et al.  Hierarchical web resources retrieval by exploiting Fuzzy Formal Concept Analysis , 2012, Inf. Process. Manag..