Self-aware Cyber-Physical Systems

Bellman, Kirstie L.; Landauer, Christopher; Dutt, Nikil; Esterle, Lukas; Herkersdorf, Andreas; Jantsch, Axel; TaheriNejad, Nima; Lewis, Peter R.; Platzner, Marco; Tammemäe, Kalle

doi:10.1145/3375716

Cited by 26 publications

(21 citation statements)

References 46 publications

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“…Slightly more complex, the device may learn an internal model of its typical charging behaviour, sufficiently to act meaningfully on, and this may adapt as the battery degrades. In more complex examples still, a cyber-physical system may have a model of available resources discovered at run-time [49].…”

Section: Building Reflective Ai Agentsmentioning

confidence: 99%

Reflective Artificial Intelligence

Lewis¹,

Sarkadi²

2023

Preprint

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Artificial Intelligence (AI) is about making computers that do the sorts of things that minds can do, and as we progress towards this goal, we tend to increasingly delegate human tasks to machines. However, AI systems usually do these tasks with an unusual imbalance of insight and understanding: new, deeper insights are present, yet many important qualities that a human mind would have previously brought to the activity are utterly absent. Therefore, it is crucial to ask which features of minds have we replicated, which are missing, and if that matters. One core feature that humans bring to tasks, when dealing with the ambiguity, emergent knowledge, and social context presented by the world, is reflection. Yet this capability is utterly missing from current mainstream AI. In this paper we ask what reflective AI might look like. Then, drawing on notions of reflection in complex systems, cognitive science, and agents, we sketch an architecture for reflective AI agents, and highlight ways forward.The definition also implies that there are things that human minds currently do, that in the future computers might do instead. This is not only true now, but has always been the case throughout human history, and likely will be far into the future. It is this transference of activity that gives rise to the seemingly constant stream of examples of new 'AI technologies'. These mostly do things that human minds used to, or wished to do. This is, of course, also the source of many of the issues and benefits that arise from the creation and use of AI technology: as we figure out how to replicate some of the things that minds can do, we delegate these things to machines. This typically brings increased automation, scale, and efficiency, which themselves contain the seeds of both enormous potential social and economic benefit, and potential real danger and strife.Further, we can notice that these AI technologies usually do this with an unusual (im)balance of insight and understanding. New, deeper insight and understanding often arise from the models employed, while many of the 'qualities' that a human mind would have previously brought to the activity, are utterly absent.In designing and analysing embodied AI technologies, the concept of an intelligent agent is central, and necessitates descriptions that are abstracted from the natural intelligences they are inspired by or modelled on. This abstraction, in turn, means that the notion of an AI agent only partially captures the mental, cognitive, and physical features of natural intelligence. Hence, it is important to ask: are the features that we have included sufficient for what is needed? Are we satisfied with leaving out those which we did?Frank and Virginia Dignum have recently reminded us how powerful the concept of an agent is in AI [2]. They have also pointed out some of the paradigmatic failures of agent-based modelling (ABM) and multi-agent systems (MAS). ABM

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Section: Building Reflective Ai Agentsmentioning

confidence: 99%

Reflective Artificial Intelligence

Lewis¹,

Sarkadi²

2023

Preprint

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“…Thereafter, the simulation of the design with well tested and validation strategies are applied for final adoption of the model in the real time setup [ 8 ]. Unlike the conventional approach to design and deployment, the authors in [ 9 ] suggest a shift from design to runtime and implementation phase for critical decisions, which depend on real time inputs and environment. Table 1 presents the various CPS simulation software and their characteristics, which can be used to test various concepts of modularity, scalability and complexity of cyber physical systems in the application domain.…”

Section: Cyber Physical Systemsmentioning

confidence: 99%

Smart City Healthcare Cyber Physical System: Characteristics, Technologies and Challenges

Verma

2021

Wireless Pers Commun

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The recent pandemic has demanded a strong and smart healthcare system which can monitor the patients efficiently and handle the situation that arises from the outbreak of the disease. Smart healthcare cyber physical systems are the future systems as they integrate the physical and cyber world for efficient functioning of medical processes and treatment through external monitoring and control of patients, medical devices and equipment for continuous communication and information exchange of physiological data. Technologies like Internet of Things, Machine learning and Artificial Intelligence have given birth to smart cyber physical systems like Smart Healthcare Systems, Smart Homes, Smart Vehicular Systems and Smart Grid. Such systems are interdisciplinary in nature with multitude of technologies contributing to its effective working. This paper presents a case study on healthcare cyber physical systems presenting its characteristics, role of various technologies in its growth and major challenges in successful implementation of cyber physical medication systems.

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“…For example, in the recent past, self-awareness showed to be a key enabler to tackle many challenges SoCs face such as rowing process variability, thermal limitations, and wearout effects [17], [18], [21]- [24]. CSA has been applied to both software [44] and hardware [52].…”

Section: Self-adaptivenessmentioning

confidence: 99%

“…Computational Self-Awareness (CSA) has been studied in a wide range of applications [16]- [18], and proved to be a key enabler of efficient resource management in different domains (e.g., sensor networks [19] and health monitoring systems [20]). It has also been proposed to tackle the challenges of comprehensive assessment in different CPSs [18], [21]- [24]. However, the community researching on selfawareness is fractioned, and research proceeds rather slow.…”

Section: Introductionmentioning

confidence: 99%

RoSA: A Framework for Modeling Self-Awareness in Cyber-Physical Systems

et al. 2020

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The role of smart and autonomous systems is becoming vital in many areas of industry and society. Expectations from such systems continuously rise and become more ambitious: long lifetime, high reliability, high performance, energy efficiency, and adaptability, particularly in the presence of changing environments. Computational self-awareness promises a comprehensive assessment of the system state for sensible and well-informed actions and resource management. Computational self-awareness concepts can be used in many applications such as automated manufacturing plants, telecommunication systems, autonomous driving, traffic control, smart grids, and wearable health monitoring systems. Developing self-aware systems from scratch for each application is the most common practice currently, but this is highly redundant, inefficient, and uneconomic. Hence, we propose a framework that supports modeling and evaluation of various self-aware concepts in hierarchical agent systems, where agents are made up of self-aware functionalities. This paper presents the Research on Self-Awareness (RoSA) framework and its design principles. In addition, self-aware functionalities abstraction, data reliability, and confidence, which are currently provided by RoSA, are described. Potential use cases of RoSA are discussed. Capabilities of the proposed framework are showcased by case studies from the fields of healthcare and industrial monitoring. We believe that RoSA is capable of serving as a common framework for self-aware modeling and applications and thus helps researchers and engineers in exploring the vast design space of hierarchical agent-based systems with computational self-awareness.

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Self-aware Cyber-Physical Systems

Cited by 26 publications

References 46 publications

Reflective Artificial Intelligence

Reflective Artificial Intelligence

Smart City Healthcare Cyber Physical System: Characteristics, Technologies and Challenges

RoSA: A Framework for Modeling Self-Awareness in Cyber-Physical Systems

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