Analytic hierarchy process in artificial life model based on fuzzy cognitive maps

Article type: Research Article

Authors: Nachazel, Tomas

Affiliations: Faculty of Informatics and Management, University of Hradec Králové, Rokitanskeho 62, 500 03, Hradec Králové, Czech Republic. E-mail: tomas.nachazel@gmail.com

Abstract: The paper describes a new approach to the modeling of individual-based artificial life models based on fuzzy cognitive maps (FCMs). The proposed concept focuses on the optimization of the artificial intelligence of individuals in multi-agent models and their adaptation to an environment. The emphasis is put on the decision-making method. FCMs offer great complexity and may be extended for learning through evolutionary algorithms. However, large FCMs suffer from high computational performance issues. This paper presents the possibility of replacing the decision-making part of an FCM with the analytic hierarchy process (AHP) method, which is widely used for decision support. Some sections in FCMs are often unused or insignificant for individuals’ behavior. Since AHP needs fewer inputs to make decisions on the same set of possible actions, this approach offers lower demands but also fewer possibilities for the development of behavior. This paper describes a transformation of an FCM into a combination of both these methods (FCM-AHP) and tests strengths and weaknesses of the approaches in the artificial life model. In comparison to the larger FCM, FCM-AHP provides a model with significantly lower computational demands while keeping nearly the same proficiency. Experiments proved that FCM-AHP has 54% lower time complexity at a price of the decrease of 4.4% in the accuracy of decision-making in comparison to the original method.

Keywords: Fuzzy cognitive maps, analytic hierarchy process, artificial life, multi-agent model, decision-making

DOI: 10.3233/AIS-180480

Journal: Journal of Ambient Intelligence and Smart Environments, vol. 10, no. 2, pp. 127-141, 2018