Using Systems Engineering Ilities to Better Understand Resiliency

Enos, James R.

doi:10.1007/978-3-030-00114-8_3

Cited by 3 publications

(5 citation statements)

References 24 publications

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“…34,36,37 From the perspective of the resilience of engineered Systems of Systems (SoS), resilience can be viewed as an inherent property of the system that is influenced by the SoS components and their behaviors, with an understanding that the resilience of a system is dynamic, and changes as the environment changes. 30 In a similar approach to the NAS definition above, 38 recognize that quality, robustness and agility are components of resilience, and identify repairability, extensibility, flexibility, adaptability, and versatility as how the system achieves robustness after a shock. In addition to these components, Haimes 39 identified also ''the ability to prepare and plan for, absorb or mitigate, recover from, or more successfully adapt to actual or potential adverse events.''…”

Section: Resilience In Dynamic and Contested Environmentsmentioning

confidence: 99%

“…Complex systems are ubiquitous, and, as their operations increasingly take place in contested and dynamic environments, so the need for their resilience becomes critical. 36,38 Modeling and simulation approaches by their nature have become increasingly popular in the analysis of the resilience of complex systems under various attacks and environmental changes, 4,40 with application domains such as energy infrastructures, 41 emergency department planning and analysis, 42 landscape ecology, 43 sociotechnical systems, 44 supply chain management, [45][46][47] and cyber-physical systems (CPS) 48 employing modeling and simulation to study various aspects of resilience. Keane et al 43 proposed the use of discrete event simulation to understand the resilience of emergency departments within hospitals that are facing earthquakes.…”

Section: Modeling Simulation Experimentation and Validationmentioning

confidence: 99%

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A simulation and experimentation architecture for resilient cooperative multiagent reinforcement learning models operating in contested and dynamic environments

Honhaga,

Szabo

2024

SIMULATION

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Cooperative multiagent reinforcement learning approaches are increasingly being used to make decisions in contested and dynamic environments, which tend to be wildly different from the environments used to train them. As such, there is a need for a more in-depth understanding of their resilience and robustness in conditions such as network partitions, node failures, or attacks. In this article, we propose a modeling and simulation framework that explores the resilience of four c-MARL models when faced with different types of attacks, and the impact that training with different perturbations has on the effectiveness of these attacks. We show that c-MARL approaches are highly vulnerable to perturbations of observation, action reward, and communication, showing more than 80% drop in the performance from the baseline. We also show that appropriate training with perturbations can dramatically improve performance in some cases, however, can also result in overfitting, making the models less resilient against other attacks. This is a first step toward a more in-depth understanding of the resilience c-MARL models and the effect that contested environments can have on their behavior and toward resilience of complex systems in general.

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Section: Resilience In Dynamic and Contested Environmentsmentioning

confidence: 99%

Section: Modeling Simulation Experimentation and Validationmentioning

confidence: 99%

A simulation and experimentation architecture for resilient cooperative multiagent reinforcement learning models operating in contested and dynamic environments

Honhaga,

Szabo

2024

SIMULATION

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“…Often these ilities do not exist in isolation and relationships between the ilities become an important factor when attempting to understand the ilities. Previous work also identifies potential linkages between resiliency and the ilites as a means to understand certain aspects of the resiliency curve as well as how engineered systems achieve resiliency . Table presents common definitions for a subset of the systems engineering ilities that apply to this article; however, various sources define the ilities slightly differently.…”

Section: Literature Reviewmentioning

confidence: 99%

“…These ilities assist systems engineers in understanding how a system's nonfunctional attributes can contribute to resiliency. Next, the section discusses the means by which systems achieve resiliency through changeability or versatility . These ilities allow systems to continue to meet stakeholder's expectations and in some cases exceed the previous performance level to demonstrate resiliency.…”

Section: Framework For Applying Ilities To Resiliencymentioning

confidence: 99%

Achieving resiliency in major defense programs through nonfunctional attributes

Enos

2019

Systems Engineering

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This article examines how the ilities, or nonfunctional attributes, help to understand the concept of resiliency in engineered systems. For engineered systems, resiliency describes the ability of a system to react to and return to full function after an interruption to system operation. The literature on resiliency of engineered systems defines resiliency in both the context of mission and platform resiliency; however, it leaves an opportunity to research how to understand, manage, and achieve resiliency. This work proposes an application of the systems engineering ilities to resiliency to understand how systems engineers can account for resiliency in the design process and incorporate resiliency into systems. Quality, robustness, and agility assist in understanding the components of resiliency and the ilities of repairability, extensibility, flexibility, adaptability, and versatility provide means for systems to achieve resiliency. This article applies this framework to examine two cases of DoD systems, the B‐52 bomber and the F‐117 stealth fighter. These two examples demonstrate how nonfunctional attributes enable engineered systems to achieve resiliency and help to better understand the concept of resiliency in engineered systems.

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“…16 for a more detailed review. While other aspects of resilience may also include: quality, agility, repairability, extensibility, flexibility, and versatility, here we focus on the three aforementioned aspects of robustness, recovery, and adaptability 16,41 . For this paper, resiliency can be described as: “a system's ability to adjust its activity to retain its basic functionality when errors, failures, and environmental changes occur.” 42 …”

Section: Introductionmentioning

confidence: 99%

A network perspective on assessing system architectures: Robustness to cascading failure

Potts

Sartor

Johnson

et al. 2020

Systems Engineering

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Despite a wealth of system architecture frameworks and methodologies available, approaches to evaluate the robustness and resiliency of architectures for complex systems or systems of systems are few in number. As a result, system architects may turn to graph‐theoretic methods to assess architecture robustness and vulnerability to cascading failure. Here, we explore the application of such methods to the analysis of two real‐world system architectures (a military communications system and a search and rescue system). Both architectures are found to be relatively robust to random vertex removal but more vulnerable to targeted vertex removal. Hardening strategies for limiting the extent of cascading failure are demonstrated to have varying degrees of effectiveness. However, in taking a network perspective on architecture robustness and susceptibility to cascade failure, we find several significant challenges that impede the straightforward use of graph‐theoretic methods. Most fundamentally, the conceptualization of failure dynamics across heterogeneous architectural entities requires considerable further investigation.

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Using Systems Engineering Ilities to Better Understand Resiliency

Cited by 3 publications

References 24 publications

A simulation and experimentation architecture for resilient cooperative multiagent reinforcement learning models operating in contested and dynamic environments

A simulation and experimentation architecture for resilient cooperative multiagent reinforcement learning models operating in contested and dynamic environments

Achieving resiliency in major defense programs through nonfunctional attributes

A network perspective on assessing system architectures: Robustness to cascading failure

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