Knowledge Based Decision Model for Architecting and Evolving Complex System‐of‐Systems

Raman, Ramakrishnan; D’Souza, Meenakshi

doi:10.1002/j.2334-5837.2017.00343.x

Cited by 7 publications

(3 citation statements)

References 15 publications

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“…There are different means to analyze the MOE relationships between the constituent systems and SoS. SoS-System MOE relationship matrix (Raman and D'Souza 2017;and Raman and D'Souza 2019) is one of the means to analyze the relationships, as indicated in Figure 2. The impact of different system MOEs on the SoS MOEs could vary.…”

Section: Measures Of Effectiveness -Moesmentioning

confidence: 99%

Framework for Formal Verification of Machine Learning Based Complex System‐of‐Systems

Raman¹,

Gupta²,

Jeppu³

2023

INSIGHT

Self Cite

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A complex system is characterized by emergence of global properties which are very difficult, if not impossible, to anticipate just from complete knowledge of component behaviors. Emergence, hierarchical organization, and numerosity are some of the characteristics of complex systems. Recently, there has been an exponential increase on the adoption of various neural network‐based machine learning models to govern the functionality and behavior of systems. With this increasing system complexity, achieving confidence in systems becomes even more difficult. Further, ease of interconnectivity among systems is permeating numerous system‐of‐systems, wherein multiple independent systems are expected to interact and collaborate to achieve unparalleled levels of functionality. Traditional verification and validation approaches are often inadequate to bring in the nuances of potential emergent behavior in a system‐of‐systems, which may be positive or negative. This paper describes a novel approach towards application of machine learning based classifiers and formal methods for analyzing and evaluating emergent behavior of complex system‐of‐systems that comprise a hybrid of constituent systems governed by conventional models and machine learning models. The proposed approach involves developing a machine learning classifier model that learns on potential negative and positive emergent behaviors, and predicts the behavior exhibited. A formal verification model is then developed to assert negative emergent behavior. The approach is illustrated through the case of a swarm of autonomous UAVs flying in a formation, and dynamically changing the shape of the formation, to support varying mission scenarios. The effectiveness and performance of the approach are quantified.

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Section: Measures Of Effectiveness -Moesmentioning

confidence: 99%

Framework for Formal Verification of Machine Learning Based Complex System‐of‐Systems

Raman¹,

Gupta²,

Jeppu³

2023

INSIGHT

Self Cite

View full text Add to dashboard Cite

show abstract

“…There are different means to analyze the MOE relationships between the constituent systems and SoS. SoS-System MOE relationship matrix (Raman and D'Souza, 2017) (Raman and D'Souza, 2019), as shown in Figure 2, is one of the means to analyze the relationships. There might be scenarios where a specific constituent system might be meeting all its MOEs, but not the SoS MOEs.…”

Section: Moe Relationship Matrixmentioning

confidence: 99%

Framework for Complex SoS Emergent Behavior Evolution Using Deep Reinforcement Learning

Raman

Murugesan

2022

INCOSE International Symp

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Advances in technology have made it easy to integrate multiple modern systems to form complex system‐of‐systems (SoS) to achieve unparalleled levels of functionality that are otherwise not achievable by the constituent systems in isolation. In fact, with the recent explosion of machine learning techniques to build autonomous systems such as drones and self‐driving cars, there is a pressing need to ensure that they collaboratively and safely operate in an SoS context. However, in general, the characteristic emergent behaviors of complex SoS – that directly impact its operational measures of success or Measures of Effectiveness (MOEs) – is very difficult, if not impossible, to manually explore, anticipate, and arbitrate just from knowledge of its underlying systems. Further, there are multiple scenarios of evolution in such complex SoS, including evolution in the emergent behavior of the SoS. The continuous, continual, and evolving nature of the SoS and constituent system environment's state and possible actions, adds further complexity. In this paper, we present a novel approach that leverages Reinforcement Learning, a machine learning approach, to inculcate adaptable intelligence in constituent systems to adapt their behaviors in tandem with the evolution of emergent behavior at the SoS level. By augmenting the reward mechanism of RL by leveraging SoS‐Constituent System MOE Relationship, that relates and ranks System MOEs vs. SoS MOEs, we inculcate an Intelligent‐Behavior Evolution Agent, with the necessary constraints to learn to maximize the SoS and system‐level MOEs, while adapting itself to the evolution in SoS. We illustrate our approach and demonstrate its feasibility and potential by applying it to a power grid SoS case example. The effectiveness and performance of the approach are quantified.

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“…There are different means to analyze the MOE relationships between the constituent systems and SoS. SoS-System MOE relationship matrix (Raman and D'Souza, 2017) (Raman and D'Souza, 2019) is one of the means to analyze the relationships, as indicated in Figure 2. The impact of different system MOEs on the SoS MOEs could vary.…”

Section: Sos Moes Vs System Moesmentioning

confidence: 99%

Framework for Formal Verification of Machine Learning Based Complex System‐of‐System

Raman

Gupta

Jeppu

2021

INCOSE International Symp

Self Cite

View full text Add to dashboard Cite

A complex system is characterized by emergence of global properties which are very difficult, if not impossible, to anticipate just from complete knowledge of component behaviors. Emergence, hierarchical organization and numerosity are some of the characteristics of complex systems. Recently, there has been an exponential increase on the adoption of various neural network‐based machine learning models to govern the functionality and behavior of systems. With this increasing system complexity, achieving confidence in systems becomes even more difficult. Further, ease of interconnectivity among systems is permeating numerous system‐of‐systems, wherein multiple independent systems are expected to interact and collaborate to achieve unparalleled levels of functionality. Traditional verification and validation approaches are often inadequate to bring in the nuances of potential emergent behavior in a system‐of‐system, which may be positive or negative. This paper describes a novel approach towards application of machine learning based classifiers and formal methods for analyzing and evaluating emergent behavior of complex system‐of‐systems that comprise a hybrid of constituent systems governed by conventional models and machine learning models. The proposed approach involves developing a machine learning classifier model that learns on potential negative and positive emergent behaviors, and predicts the behavior exhibited. A formal verification model is then developed to assert negative emergent behavior. The approach is illustrated through the case of a swarm of autonomous UAVs flying in a formation, and dynamically changing the shape of the formation, to support varying mission scenarios. The effectiveness and performance of the approach are quantified.

show abstract

Knowledge Based Decision Model for Architecting and Evolving Complex System‐of‐Systems

Cited by 7 publications

References 15 publications

Framework for Formal Verification of Machine Learning Based Complex System‐of‐Systems

Framework for Formal Verification of Machine Learning Based Complex System‐of‐Systems

Framework for Complex SoS Emergent Behavior Evolution Using Deep Reinforcement Learning

Framework for Formal Verification of Machine Learning Based Complex System‐of‐System

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