迈向可信赖的网络物理生产系统:动态代理问责方法

IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Richárd Beregi, G. Pedone, D. Preuveneers
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引用次数: 1

摘要

智能制造是工业4.0范式所孕育的一个具有挑战性的趋势。在这种情况下,多代理系统(Multi-Agent Systems, MAS)被特别选择用于建模这类智能、分散的过程,这要归功于它们在追求集体和合作目标方面的自主性。然而,从人类的角度来看,提高对基于MAS的信息物理生产系统(CPPS)的可信度的信心仍然是一个重大挑战。制造服务必须在可靠性、健壮性和延迟方面符合严格的要求,并且解决方案提供商应确保代理将在生产的特定边界内操作,并在制造活动执行期间减少无人值守的行为。为了解决这个问题,提出了一个制造代理责任框架,这是一个动态授权框架,它定义并强制边界,在这个边界中,代理可以自由地利用他们的智能来达到个人和集体的目标。代理的预期行为是遵守CPPS工作流,该工作流隐含地定义了行为的可接受区域和生产可行性。提出的框架的核心贡献是:制造问责模型,代理行为自治治理的叶图表示,以及用于识别和避免CPPS服务执行中的恶意行为的声明性策略本体。我们概述了这种增强的可信度框架在各种手工工具生产的基于代理的制造用例中的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Towards trustworthy Cyber-physical Production Systems: A dynamic agent accountability approach
Smart manufacturing is a challenging trend being fostered by the Industry 4.0 paradigm. In this scenario Multi-Agent Systems (MAS) are particularly elected for modeling such types of intelligent, decentralised processes, thanks to their autonomy in pursuing collective and cooperative goals. From a human perspective, however, increasing the confidence in trustworthiness of MAS based Cyber-physical Production Systems (CPPS) remains a significant challenge. Manufacturing services must comply with strong requirements in terms of reliability, robustness and latency, and solution providers are expected to ensure that agents will operate within certain boundaries of the production, and mitigate unattended behaviours during the execution of manufacturing activities. To address this concern, a Manufacturing Agent Accountability Framework is proposed, a dynamic authorization framework that defines and enforces boundaries in which agents are freely permitted to exploit their intelligence to reach individual and collective objectives. The expected behaviour of agents is to adhere to CPPS workflows which implicitly define acceptable regions of behaviours and production feasibility. Core contributions of the proposed framework are: a manufacturing accountability model, the representation of the Leaf Diagrams for the governance of agent behavioural autonomy, and an ontology of declarative policies for the identification and avoidance of ill-intentioned behaviours in the execution of CPPS services. We outline the application of this enhanced trustworthiness framework to an agent-based manufacturing use-case for the production of a variety of hand tools.
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来源期刊
Journal of Ambient Intelligence and Smart Environments
Journal of Ambient Intelligence and Smart Environments COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
4.30
自引率
17.60%
发文量
23
审稿时长
>12 weeks
期刊介绍: The Journal of Ambient Intelligence and Smart Environments (JAISE) serves as a forum to discuss the latest developments on Ambient Intelligence (AmI) and Smart Environments (SmE). Given the multi-disciplinary nature of the areas involved, the journal aims to promote participation from several different communities covering topics ranging from enabling technologies such as multi-modal sensing and vision processing, to algorithmic aspects in interpretive and reasoning domains, to application-oriented efforts in human-centered services, as well as contributions from the fields of robotics, networking, HCI, mobile, collaborative and pervasive computing. This diversity stems from the fact that smart environments can be defined with a variety of different characteristics based on the applications they serve, their interaction models with humans, the practical system design aspects, as well as the multi-faceted conceptual and algorithmic considerations that would enable them to operate seamlessly and unobtrusively. The Journal of Ambient Intelligence and Smart Environments will focus on both the technical and application aspects of these.
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