An opponent model for agent-based shared decision-making via a genetic algorithm.

Autor:	Lin KB; School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China., Wei Y; School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China., Liu Y; School of Data Science and Intelligent Engineering, Xiamen Institute of Technology, Xiamen, China., Hong FP; Department of Neonates, Xiamen Humanity Hospital, Xiamen, China., Yang YM; Department of Pediatrics, Xiamen Hospital of Traditional Chinese Medicine, Xiamen, China., Lu P; School of Economics and Management, Xiamen University of Technology, Xiamen, China.
Jazyk:	angličtina
Zdroj:	Frontiers in psychology [Front Psychol] 2023 Oct 03; Vol. 14, pp. 1124734. Date of Electronic Publication: 2023 Oct 03 (Print Publication: 2023).
DOI:	10.3389/fpsyg.2023.1124734
Abstrakt:	Introduction: Shared decision-making (SDM) has received a great deal of attention as an effective way to achieve patient-centered medical care. SDM aims to bring doctors and patients together to develop treatment plans through negotiation. However, time pressure and subjective factors such as medical illiteracy and inadequate communication skills prevent doctors and patients from accurately expressing and obtaining their opponent's preferences. This problem leads to SDM being in an incomplete information environment, which significantly reduces the efficiency of the negotiation and even leads to failure. Methods: In this study, we integrated a negotiation strategy that predicts opponent preference using a genetic algorithm with an SDM auto-negotiation model constructed based on fuzzy constraints, thereby enhancing the effectiveness of SDM by addressing the problems posed by incomplete information environments and rapidly generating treatment plans with high mutual satisfaction. Results: A variety of negotiation scenarios are simulated in experiments and the proposed model is compared with other excellent negotiation models. The results indicated that the proposed model better adapts to multivariate scenarios and maintains higher mutual satisfaction. Discussion: The agent negotiation framework supports SDM participants in accessing treatment plans that fit individual preferences, thereby increasing treatment satisfaction. Adding GA opponent preference prediction to the SDM negotiation framework can effectively improve negotiation performance in incomplete information environments. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2023 Lin, Wei, Liu, Hong, Yang and Lu.)
Databáze:	MEDLINE
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