Xiao Liang, Greer Des
School of Computer Science, Hubei University of Technology, Wuhan 430068, China.
School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast BT7 1NN, UK.
Healthcare (Basel). 2023 Feb 15;11(4):585. doi: 10.3390/healthcare11040585.
Multidisciplinary clinical decision-making has become increasingly important for complex diseases, such as cancers, as medicine has become very specialized. Multiagent systems (MASs) provide a suitable framework to support multidisciplinary decisions. In the past years, a number of agent-oriented approaches have been developed on the basis of argumentation models. However, very limited work has focused, thus far, on systematic support for argumentation in communication among multiple agents spanning various decision sites and holding varying beliefs. There is a need for an appropriate argumentation scheme and identification of recurring styles or patterns of multiagent argument linking to enable versatile multidisciplinary decision applications. We propose, in this paper, a method of linked argumentation graphs and three types of patterns corresponding to scenarios of agents changing the minds of others (argumentation) and their own (belief revision): the collaboration pattern, the negotiation pattern, and the persuasion pattern. This approach is demonstrated using a case study of breast cancer and lifelong recommendations, as the survival rates of diagnosed cancer patients are rising and comorbidity is the norm.
随着医学变得高度专业化,多学科临床决策对于癌症等复杂疾病而言变得愈发重要。多智能体系统(MASs)提供了一个支持多学科决策的合适框架。在过去几年中,基于论证模型开发了许多面向智能体的方法。然而,到目前为止,针对跨越不同决策地点且持有不同信念的多个智能体之间通信中的论证进行系统支持的工作非常有限。需要一种合适的论证方案以及识别多智能体论证的反复出现的风格或模式,以实现通用的多学科决策应用。在本文中,我们提出了一种关联论证图的方法以及与智能体改变他人想法(论证)和改变自身想法(信念修正)的场景相对应的三种模式:协作模式、协商模式和说服模式。通过乳腺癌及终身建议的案例研究来展示这种方法,因为确诊癌症患者的生存率在上升且合并症很常见。