Computer Science Department, Faculty of Computer Science and IT, University of Lahore, Lahore, Pakistan.
School of Software, Dalian University of Technology, Dalian, China.
Front Public Health. 2022 Mar 3;10:849185. doi: 10.3389/fpubh.2022.849185. eCollection 2022.
In the last decade, smart computing has garnered much attention, particularly in ubiquitous environments, thus increasing the ease of everyday human life. Users can dynamically interact with the systems using different modalities in a smart computing environment. The literature discussed multiple mechanisms to enhance the modalities for communication using different knowledge sources. Among others, Multi-context System (MCS) has been proven quite significant to interlink various context domains dynamically to a distributed environment. MCS is a collection of different contexts (independent knowledge sources), and every context contains its own set of defined rules and facts and inference systems. These contexts are interlinked bridge rules. However, the interaction among knowledge sources could have the consequences such as bringing out inconsistent results. These issues may report situations such as the system being unable to reach a conclusion or communication in different contexts becoming asynchronous. There is a need for a suitable framework to resolve inconsistencies. In this article, we provide a framework based on contextual defeasible reasoning and a formalism of multi-agent environment is to handle the issue of inconsistent information in MCS. Additionally, in this work, a prototypal simulation is designed using a simulation tool called NetLogo, and a formalism about a Parkinson's disease patient's case study is also developed. Both of these show the validity of the framework.
在过去的十年中,智能计算受到了广泛关注,特别是在无处不在的环境中,这极大地提高了人们日常生活的便利性。用户可以在智能计算环境中使用不同的模式与系统进行动态交互。文献讨论了多种机制,以利用不同的知识源增强通信模式。其中,多上下文系统(MCS)已被证明对于动态链接不同上下文域到分布式环境非常重要。MCS 是不同上下文(独立的知识源)的集合,每个上下文都包含自己的一组定义规则、事实和推理系统。这些上下文通过桥接规则相互链接。然而,知识源之间的交互可能会产生不一致的结果。这些问题可能会导致系统无法得出结论或不同上下文中的通信变得异步等情况。因此,需要一个合适的框架来解决不一致性问题。在本文中,我们提供了一个基于上下文可废止推理的框架和一个多智能体环境的形式化方法,用于处理 MCS 中不一致信息的问题。此外,在这项工作中,还使用名为 NetLogo 的仿真工具设计了一个原型仿真,并开发了一个关于帕金森病患者病例研究的形式化方法。这两者都展示了该框架的有效性。