Abstract
In the last years we have seen a large development about fuzzy cognitive maps (FCMs). The theoretical base of FCM is connected with the neural networks theory (differential hebbian learning) and fuzzy sets and systems theory. There are applications of the FCM in the following fields of research: adaptive network cognitive processor, analyze and extend graph theoretical behavior, plant control, information requirement analysis, analysis electrical circuit, model gastric-appetite behavior and popular political development. Our interest in this work is to show the importance of combination of FCM with the multi-agent modal logic of knowledge and belief to model structures of complex design in multiagent environments. We make an application to plant control. The model that we introduce can be used to model popular political development, social systems, and military strategy, and others. In Goto and Yamaguchi [1991] is shown as FCMs can model plant control. In our point of view an ideal model of plant control must not involve the opinion of an expert about a plant control, but the opinion of a group of experts. If each expert of a group makes a FCM on a plant control, it is interesting to investigate what signifies the common knowledge of group about this plant control. Moreover is interesting to know what each agent of group knows about the FCM made by others agents. This can be much important in the design of plant control and to build political model, social systems, and military strategy. With reference to multi-agent modal logic of knowledge and belief, we make a generalization of work of Friedman and Halpern [1994a], Friedman and Halpern [1994b], by using of fuzzy measures. The multi-agent modal logic of knowledge and belief with fuzzy measures allows interpret fuzzy statements with linguistic fuzzy quantifiers such as developed in FCM.
This is a preview of subscription content, log in via an institution.
Preview
Unable to display preview. Download preview PDF.
References
HALPERN, J., & MOSES, Y. [1992]-A Guide to Completeness and Complexity for for Modal Logic of Knowledge and Belief, Artificial Intelligence Vol. 54, No. 3, april 319–379.
TABER, W. R. [1991]-Knowledge Processing with Fuzzy Cognitive Maps, Expert Systems with Applications, Vol. 2, 83–87.
EDSON, B., TURNER, C., MEYERS, M., & SIMPSON, P.[1988]-The Adaptive Networks Cognitive Processor, Proceedings of the 1988 Aerospace Applications of Artificial Intelligence (AAAIC 88), Vol.II.
MYERS, M., TURNER, C., KUCZEWSKI, R., & SIMPSON, P. [1988]-ANCP Adaptive Network Cognitive Processor: Vols I & II, TRW MEAD, Final Report Prepared for Air Force Wright Aeronautical Laboratories
STYBLINSKI, M. & MEYER, B. [1988]-Fuzzy Cognitive Map, Signal Flow Graphs, and Qualitative Circuit Analysis, Proceedings of the IEEE International Conference on Neural Network: Vol. II, (pp. 549–556). San Diego: IEEE.
MENTAZENI, A. & CONRATH, D. [1986]-The Use of Cognitive Mapping for Information Requirement Analysis, Management Information Systems Quarterly.
SUGENO, M.[1977]-Fuzzy Measures and Fuzzy Integrals — a survey, in Gupta, M.M., Saridis, G.N., and Gaines, B.R.[1977].
GUPTA, M.M., et. al.[1977]-Fuzzy Automata and Decision Processes, North-Holland, New York.
DUBOIS, H. & PRADE, D. [1982]-A Class of Fuzzy Measures based on Triangular Norms, International, J. of General Systems, 8.
KOSKO, B. [1992]-Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence, Prentice Hall, New York.
STYBLINSKI, M. & MEYER, B. [1991]-Signal Flow Graphs vs Fuzzy Cognitive in Application to Qualitative Circuit Analysis, International Journal of Man-Machine Studies, 35, 175–186.
GOTO, K., & MURAKAMI, J., YAMAGUCHI, T., & YAMANAKA, Y., [1989]-Application of Fuzzy Cognitive Maps to Supporting for Plant Control, (in Japanese) 10th Knowledge Engineering Symposium, 99–104.
GOTO, K., & YAMAGUCH. T.[1991]-Fuzzy Associative Memory Application to a Plant Modeling, in Kohonen, K., Makisara, O. Simula, O., and Kangas, J. [1991]
KOHONEN, K., MAKISARA, O. SIMULA, O., & KANGAS, J. [1991]-Artificial Neural Networks, Vol.2, North-Holland.
ZHANG, W., & CHEN, S. [1988]-A Logical Architecture for Cognitive Maps, Proceedings of the 2nd IEEE International Conference on Neural Network, Vol. I, 231–238, july.
FRIEDMAN, N. & HALPERN, J.[1994a]-A knowledge-Based Framework For Belief Change Part I: Foundations, Proceedings of Theoretical Aspects of Reasoning about Knowledge, Morgan Kaufman.
FRIEDMAN, N. & HALPERN, J.[1994b]-A Knowledge-Based Framework For Belief Change Part II: Revision and Update, Principles of Knowledge Representation and Reasoning: Proc. Fourth International Conference (KR'94)
FRIEDMAN, N. & HALPERN, J.[1994c]-On the Complexity of Conditional Logics, in Principles of Knowledge Representation and Reasoning: Proc. Fourth International Conference (KR'94).
ZADEH, L.A.[1978]-Fuzzy sets as a Basis for a Theory of Possibility, Fuzzy Sets and Systems, Vol. 1, No. 1, pp. 3–28.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1995 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Silva, P.C. (1995). Fuzzy cognitive maps in multi-agent environments. In: Gori, M., Soda, G. (eds) Topics in Artificial Intelligence. AI*IA 1995. Lecture Notes in Computer Science, vol 992. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60437-5_4
Download citation
DOI: https://doi.org/10.1007/3-540-60437-5_4
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-60437-2
Online ISBN: 978-3-540-47468-5
eBook Packages: Springer Book Archive