Authors:
Andreas Christoforou
Affiliation:
Department of Electrical Engineering, Computer Engineering and Informatics, Cyprus University of Technology, Limassol, Cyprus
Keyword(s):
Multi-Layer Fuzzy Cognitive Maps, Explainable Decision Support, Multi-Objective Optimization, Microservices Adoption.
Abstract:
The tremendous progress in the field of artificial and computational intelligence has enabled the application of relevant techniques to a wide range of human life aspects. However, these techniques appear in their majority incompetent to allow users to explain and understand their decisions. This paper introduces an enhanced, explainable decision support approach using a promising graph-based computational intelligent model, namely Multi-Layer Fuzzy Cognitive Maps (MLFCM). MLFCM have evolved over the last two decades into a flexible and powerful tool that enables the execution of simulation scenarios to facilitate decision support in highly complex environments. The proposed enhancement of MLFCM revolves around their integration with MultiObjective Evolutionary Algorithms that allows executing simulations with multiple conflicting targets and then analyzing the values and relationships of the participating nodes. The applicability of the enhanced MLFCM is demonstrated through a case-
study on adopting microservices. Microservices have been considered as one of the most promising alternatives to software development nowadays. Nevertheless, their adoption often stumbles on various factors such as security, exit policy, effectiveness, etc. In this context, the factors contributing to Microservices adoption are assessed, analyzed and modeled via MLFCM using a series of real-world and synthetic scenarios that yielded quite promising results.
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