Intelligent modeling with agent-based fuzzy cognitive map

This article presents an agent-based fuzzy cognitive map (ABFCM) developed injecting the concept of multi-agent system (MAS) into the fuzzy cognitive map (FCM). Fuzzy cognitive map is used for qualitative modeling and simulation. Compared to the FCM, the ABFCM enables different inference algorithms in each node enabling simulation of systems with diverse behavior concepts. Each map node can exhibit individual, more or less intelligent, behavior and still can interact with other nodes to conclude on system behavior. Resulting method also enables automatic results interpretation adding additional intelligence to a classic FCM. Explanation of the obtained system architecture with FCM and MAS integration is presented in the article. The experimental results in the article are obtained with the ABFCM prototype, developed on the basis of ABFCM structure given in the article. Multi-agent technology can bring new properties into existing fields and methods, like in the ABFCM case. © 2010 Wiley Periodicals, Inc.

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

[2]  Maja Štula,et al.  AGENT BASED FUZZY COGNITIVE MAPS IN FIRE FIGHTING DECISION SUPPORT , 2007 .

[3]  J. P. Carvalho,et al.  Forest Fire Modelling using Rule-Based Fuzzy Cognitive Maps and Voronoi Based Cellular Automata , 2006, NAFIPS 2006 - 2006 Annual Meeting of the North American Fuzzy Information Processing Society.

[4]  Soung Hie Kim,et al.  Fuzzy cognitive maps considering time relationships , 1995, Int. J. Hum. Comput. Stud..

[5]  Charles J. Petrie,et al.  Agent-Based Engineering, the Web, and Intelligence , 1996, IEEE Expert.

[6]  Chrysostomos D. Stylios,et al.  Fuzzy Cognitive Maps in modeling supervisory control systems , 2000, J. Intell. Fuzzy Syst..

[7]  J.A.B. Tome,et al.  Rule based fuzzy cognitive maps-qualitative systems dynamics , 2000, PeachFuzz 2000. 19th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.00TH8500).

[8]  Henrik Eriksson,et al.  The evolution of Protégé: an environment for knowledge-based systems development , 2003, Int. J. Hum. Comput. Stud..

[9]  Kun Chang Lee,et al.  Fuzzy implications of fuzzy cognitive map with emphasis on fuzzy causal relationship and fuzzy partially causal relationship , 1998, Fuzzy Sets Syst..

[10]  Nicholas R. Jennings,et al.  On Agent-Mediated Electronic Commerce , 2003, IEEE Trans. Knowl. Data Eng..

[11]  Agostino Poggi,et al.  Developing Multi-agent Systems with JADE , 2007, ATAL.

[12]  Florentin Smarandache,et al.  FUZZY COGNITIVE MAPS AND NEUTROSOPHIC COGNITIVE MAPS , 2003, math/0311063.

[13]  Nicholas R. Jennings,et al.  Intelligent agents: theory and practice , 1995, The Knowledge Engineering Review.

[14]  Zhi-Qiang Liu,et al.  On causal inference in fuzzy cognitive maps , 2000, IEEE Trans. Fuzzy Syst..

[15]  Steffen Staab,et al.  Knowledge Processes and Ontologies , 2001, IEEE Intell. Syst..

[16]  Mark A. Musen,et al.  The Knowledge Model of Protégé-2000: Combining Interoperability and Flexibility , 2000, EKAW.

[17]  Masafumi Hagiwara,et al.  Extended fuzzy cognitive maps , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.

[18]  Konstantinos G. Margaritis,et al.  Cognitive Mapping and Certainty Neuron Fuzzy Cognitive Maps , 1997, Inf. Sci..

[19]  Rod Taber,et al.  Knowledge processing with Fuzzy Cognitive Maps , 1991 .

[20]  M. Karam,et al.  Modeling a simple inverted pendulum using a model-based dynamic recurrent neural network , 2005, Proceedings of the Thirty-Seventh Southeastern Symposium on System Theory, 2005. SSST '05..

[21]  Balakrishnan Chandrasekaran,et al.  What are ontologies, and why do we need them? , 1999, IEEE Intell. Syst..

[22]  Bart Kosko,et al.  Neural networks and fuzzy systems , 1998 .

[23]  Agostino Poggi,et al.  Jade - a fipa-compliant agent framework , 1999 .

[24]  Nicholas R. Jennings,et al.  A Roadmap of Agent Research and Development , 2004, Autonomous Agents and Multi-Agent Systems.

[25]  Zhi-Qiang Liu,et al.  Contextual fuzzy cognitive map for decision support in geographic information systems , 1999, IEEE Trans. Fuzzy Syst..

[26]  Athanasios K. Tsadiras,et al.  Comparing the inference capabilities of binary, trivalent and sigmoid fuzzy cognitive maps , 2008, Inf. Sci..

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