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Decision learning framework for architecture design decisions of complex systems and system-of-systems

机译：用于复杂系统和系统的体系结构设计决策的决策学习框架

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摘要

Architecting complex systems and complex system-of-systems (SoS) have evinced keen interest recently. Architectural design decisions have a significant bearing on the operational measures of success, referred to as Measures of Effectiveness (MOEs), of the systems and SoS. Architecting complex systems and SoS involves making architecture design decisions despite uncertainty (due to knowledge gaps) on the implications associated with the decisions. The learning of whether the decision is optimal or not, and the impact on the MOEs and the emergent behavior of the SoS, often occur later, resulting in Learning Cycles. This paper proposes an integrated decision learning framework for architecture design decisions for complex systems and SoS. The proposed framework adopts a decision oriented view that factors the uncertainty associated with architectural decisions and the learning cycles and feedback loops experienced. The framework enables leverage of machine learning approaches to learn from the decision learning cycles experienced and factor it into the uncertainty assessments of the decisions. By inculcating various aspects such as knowledge gaps and learning cycles, by building models such as Learning Cycle Model and Uncertainty Model, and by incorporating deployment approaches such as codification of decision attributes and decision uncertainty assessments, the proposed framework enables progressive maturity of the architectural knowledge base and aids robustness in architecture design decisions.
机译：最近，设计复杂系统和复杂系统系统（SoS）引起了人们的极大兴趣。体系结构设计决策与系统和SoS的成功操作度量（称为有效性度量（MOE））有很大关系。架构复杂的系统和SoS时，尽管存在不确定性（由于知识鸿沟），但仍需做出架构设计决策，而这些决策所涉及的含义。关于决策是否最佳以及对MOE和SoS紧急行为的影响的学习通常会在稍后发生，从而导致学习周期。本文为复杂系统和SoS的体系结构设计决策提出了一个集成的决策学习框架。所提出的框架采用面向决策的观点，该观点考虑了与体系结构决策以及学习周期和反馈回路相关的不确定性。该框架可以利用机器学习方法从经验丰富的决策学习周期中学习，并将其纳入决策的不确定性评估中。通过灌输知识差距和学习周期等各个方面，通过构建学习周期模型和不确定性模型等模型，并通过合并决策属性和决策不确定性评估之类的部署方法，所提出的框架可以使体系结构知识逐步成熟在体系结构设计决策中提供基础并增强鲁棒性。

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