DReAM: Dynamic Reconfigurable Architecture Modeling

Nicola, Rocco De; Maggi, Alessandro; Sifakis, Joseph

doi:10.1007/978-3-030-03424-5_2

Cited by 11 publications

(3 citation statements)

References 17 publications

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“…The model is inspired from the BIP coordination language. It has been studied and implemented in two formalisms, one declarative and another imperative [3,4,5].…”

Section: A Computational Model For Autonomous Systems 21 a System Arc...mentioning

confidence: 99%

“…Self-organization is the ability to adapt coordination rules to changing system dynamics. We have shown that all these types of architectures can be formalized as operators taking as arguments arbitrary numbers of instances of agent and objects types [3,4,5]. We badly need theory for studying their properties in a compositional manner.…”

Section: Architecture Complexitymentioning

confidence: 99%

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Autonomous Systems – An Architectural Characterization

Sifakis

2019

Lecture Notes in Computer Science

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The concept of autonomy is key to the IoT vision promising increasing integration of smart services and systems minimizing human intervention. This vision challenges our capability to build complex open trustworthy autonomous systems. We lack a rigorous common semantic framework for autonomous systems. It is remarkable that the debate about autonomous vehicles focuses almost exclusively on AI and learning techniques while it ignores many other equally important autonomous system design issues.Autonomous systems involve agents and objects coordinated in some common environment so that their collective behavior meets a set of global goals. We propose a general computational model combining a system architecture model and an agent model. The architecture model allows expression of dynamic reconfigurable multi-mode coordination between components. The agent model consists of five interacting modules implementing each one a characteristic function: Perception, Reflection, Goal management, Planning and Self-adaptation. It determines a concept of autonomic complexity accounting for the specific difficulty to build autonomous systems.We emphasize that the main characteristic of autonomous systems is their ability to handle knowledge and adaptively respond to environment changes. We advocate that autonomy should be associated with functionality and not with specific techniques. Machine learning is essential for autonomy although it can meet only a small portion of the needs implied by autonomous system design. We conclude that autonomy is a kind of broad intelligence. Building trustworthy and optimal autonomous systems goes far beyond the AI challenge.

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“…The model is inspired from the BIP coordination language. It has been studied and implemented in two formalisms, one declarative and another imperative [3,4,5].…”

Section: A Computational Model For Autonomous Systems 21 a System Arc...mentioning

confidence: 99%

Section: Architecture Complexitymentioning

confidence: 99%

Autonomous Systems – An Architectural Characterization

Sifakis

2019

Lecture Notes in Computer Science

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“…We characterize the complexity of architectures by the degree to which all these attributes may dynamically change and be organized, as illustrated in Figure 5. This classification distinguishes five types of architectures and has been carried out based on technical criteria presented in [12,13]. 5.…”

Section: Architecture Complexity -Self-organizing Architecturesmentioning

confidence: 99%

System Design in the Era of IoT — Meeting the Autonomy Challenge

Sifakis

2018

Electron. Proc. Theor. Comput. Sci.

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The advent of IoT is a great opportunity to reinvigorate Computing by focusing on autonomous system design. This certainly raises technology questions but, more importantly, it requires building new foundation that will systematically integrate the innovative results needed to face increasing environment and mission complexity.A key idea is to compensate the lack of human intervention by adaptive control. This is instrumental for system resilience: it allows both coping with uncertainty and managing mixed criticality services. Our proposal for knowledge-based design seeks a compromise: preserving rigorousness despite the fact that essential properties cannot be guaranteed at design time. It makes knowledge generation and application a primary concern and aims to fully and seamlessly incorporate the adaptive control paradigm in system architecture.

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