Soft Computing: Theory Innovations and Problem-Solving Benefits
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Item An Innovative Hybrid Soft Consensus Framework Leveraging Generative Large Language Models and Human Expertise(2025-01-07) Perez Gálvez, Ignacio; Bernabe-Moreno, Juan; Herrera-Viedma, Enrique; Cabrerizo, FranciscoTraditional group decision making models have often used artificial intelligence tools, such as fuzzy logic, to act as moderators among multiple human experts tasked with selecting the best option from a set of alternatives. These models facilitated consensus by interpreting and integrating diverse expert opinions. However, the advent of generative Artificial Intelligence models introduces a new challenge: how to integrate these advanced models as active participants in the decision group, alongside human experts. This paper proposes a new hybrid consensus framework that integrates generative large language models as key contributors to the negotiation process. By taking advantage of the unique strengths of both human expertise and AI-driven insights, our framework aims to enhance the robustness and efficiency of group decision making. We explore methods for effectively integrating generative models, addressing potential biases, and ensuring coherent collaboration between human and AI participants. This approach not only enriches the decision making process, but also sets a precedent for future collaborative systems combining human knowledge and artificial intelligence.Item Introduction to the Minitrack on Soft Computing: Theory Innovations and Problem-Solving Benefits(2025-01-07) Cabrerizo, Francisco; Perez Gálvez, Ignacio; Herrera-Viedma, EnriqueItem LA-COMET: Toward Reducing Redundant Criteria in Multi-criteria Decision Analysis(2025-01-07) Kizielewicz, Bartłomiej; Więckowski, Jakub; Sałabun, Wojciech; Wątróbski, JarosławMulti-Criteria Decision Analysis (MCDA) is an effective tool for decision-making in complex situations characterized by multiple, often conflicting, criteria. However, identifying and determining the relevance of individual criteria can be complicated, especially when the number of criteria is excessively high or when decision experts are not readily available. This paper presents LA-COMET, an innovative method for reducing redundant criteria in MCDA. LA-COMET combines the Characteristic Objects Method (COMET), based on expert preference modeling, with the Regression Lasso technique for automatically selecting relevant variables. Through this combination, our method effectively reduces the number of criteria while maintaining relevant decision-making information. In this method, the Lasso approach also plays the role of an artificial expert, which relies on previous sample evaluations to support COMET model identification. We also present the results of our study, in which we compare the effectiveness of LA-COMET with traditional MCDA methods. In a practical case, the problem of evaluating countries regarding their military potential is considered, where we attempt to re-identify such a model. Our experiments show that LA-COMET achieves significantly better results than classical methods, ensuring high accuracy and relevance of the decisions made.