An AI-Driven Multi-Criteria Decision Support Framework for Explainable Decision-Making under Uncertainty
Keywords:
Artificial intelligence, Decision support system, Explainable AI, Fuzzy logic, Intelligent decision-making, Machine learning, Multi-criteria decision-making, Predictive analyticsAbstract
Decision-making in the context of modern organizations can be regarded as a complex process due to multiple factors, data volumes, contradicting criteria, uncertainty, and dynamic conditions. Therefore, it is essential to consider advanced methods for analyzing, prioritizing, comparing, and selecting alternatives. This study aims to propose an idea for an AI-based Multi-Criteria Decision Support Framework for Intelligent Decision-Making in Dynamic Environments based on the principles of artificial intelligence, machine learning, Multi-Criteria Decision Support, fuzzy logic, data management, and visualization techniques. The suggested framework could be considered a structure supporting the process of decision-making through analyzing alternatives on multiple criteria and providing recommendations. The programming language Python could be used as an efficient environment for implementing numerical calculations, data science, machine learning, and visualization. In particular, NumPy and Pandas are efficient tools for mathematical functions, statistics, and working with data, while Scikit-learn and SciPy provide machine learning algorithms, optimization, and statistical functions. Moreover, the Python language has libraries for creating visual content for further presentation and exploration of results. The framework could implement machine learning algorithms such as Logistic Regression, Support Vector Machine, k-Nearest Neighbors, Random Forest, and XGBoost for classification, prediction, risk assessment, and pattern recognition. The Analytic Hierarchy Process, TOPSIS, VIKOR, and EDAS methods could be applied for multi-criteria analysis of alternatives, while fuzzy logic could be used for representing uncertainty in natural terms.
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