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Fuzzy cognitive maps for decision-making in dynamic environments

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Abstract

This paper describes a new modification of fuzzy cognitive maps (FCMs) for the modeling of autonomous entities that make decisions in a dynamic environment. The paper offers a general design for an FCM adjusted for the decision-making of autonomous agents through the categorization of its concepts into three different classes according to their purpose in the map: Needs, Activities, and States (FCM-NAS). The classification enables features supporting decision-making, such as the easy processing of input from sensors, faster system reactions, the modeling of inner needs, the adjustable frequency of computations in a simulation, and self-evaluation of the FCM-NAS that supports unsupervised evolutionary learning. This paper presents two use cases of the proposed extension to demonstrate its abilities. It was implemented into an agent-based artificial life model, where it took advantage of all the above features in the competition for resources, natural selection, and evolution. Then, it was used as decision-making for human activity simulation in an ambient intelligence model, where it is combined with scenario-oriented mechanism proving its modularity.

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Acknowledgments

Support from the Excellence Project “Decision Support Systems: Principles and Applications 2” in the Faculty of Informatics and Management, University of Hradec Králové, is gratefully acknowledged.

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  1. Faculty of Informatics and Management, University of Hradec Králové, Rokitanskeho 62, 500 03, Hradec Králové, Czech Republic

    Tomas Nachazel

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  1. Tomas Nachazel
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Correspondence to Tomas Nachazel.

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Appendix

Appendix

The following pseudocode includes the functions of the computation of FCM-NAS needed to process values every time step:

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Nachazel, T. Fuzzy cognitive maps for decision-making in dynamic environments. Genet Program Evolvable Mach 22, 101–135 (2021). https://doi.org/10.1007/s10710-020-09393-2

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  • DOI: https://doi.org/10.1007/s10710-020-09393-2

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