基础设施网络风险评估与管理中agent协同决策策略研究

C. Gómez, Diego Castiblanco, M. Sánchez-Silva, L. Dueñas-Osorio
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引用次数: 0

摘要

物理基础设施网络是复杂的分布式系统,对社会至关重要。决策过程中的人为因素使基础设施工程成为一个社会技术问题,通常以分散的方式进行,特别是在重大中断期间。作者之前介绍了复杂分布式代理网络(CoDAN)框架,用于复杂系统中基于风险的决策。在用CoDAN分析基础设施系统时,传统的物理和网络特征与组织决策过程相结合。后者是通过将代理功能分配给网络中的某些子单元来实现的,这些子单元是通过提供合理物理分区的模式识别技术检测到的。CoDAN探讨了代理之间的协调,但没有考虑合作,这可能会导致改进。本文探讨了与基础设施网络子单元相关的CoDAN代理之间的协作,这些代理执行风险评估并对网络运行做出准最优决策。本文的贡献在于允许和研究代理之间的信息交换策略,确定代理共享哪些信息以及与谁共享信息的含义。说明性的例子表明,智能体之间的相互作用经常导致其优化目标的改进(例如,增强可靠性);通过智能地选择与哪些代理协作,可以获得进一步的改进。在实际应用中,必须考虑agent传输和处理信息的成本,以及在真实环境中实现这些策略可能遇到的障碍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Study of policies for agent collaborative decision-making in risk assessment and management of infrastructure networks
Physical infrastructure networks are complex distributed systems which are vital for society. The human aspect of decision-making processes makes infrastructure engineering a socio-technical problem, which is commonly carried out in a decentralised way, particularly during major disruptions. The authors previously introduced the Complex Distributed Agent Network (CoDAN) framework for risk-based decision-making in complex systems. When analysing infrastructure systems with CoDAN, traditional physical and network features are combined with organisational decision-making processes involved in their operation. The latter is achieved by assigning agent capabilities to certain sub-units in the network, which are detected via pattern recognition techniques that provide reasonable physical partitions. CoDAN explored coordination amongst agents but did not consider cooperation, which may lead to improvements. This paper explores collaboration amongst CoDAN agents associated to infrastructure network sub-units, which perform risk assessment and make quasi-optimal decisions about network operation. The contribution of this paper is to allow and study information exchange policies amongst agents, determining the implications of which information agents share and with whom they share it. Illustrative examples suggest that interactions amongst agents frequently leads to improvements in their optimisation objectives (e.g., enhancing reliability); further improvements can be obtained by intelligently selecting with which agents to collaborate. For practical applications, it is essential to consider the cost for agents to transmit and process information as well as the possible obstacles to implement such policies in real environments.
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