Local Scheduling in Multi-Agent Systems: Getting Ready for Safety-Critical Scenarios

Calvaresi, Davide; Marinoni, Mauro; Lustrissimini, Luca; Appoggetti, Kevin; Sernani, Paolo; Dragoni, Aldo Franco; Schumacher, Michael; Buttazzo, Giorgio

doi:10.1007/978-3-030-01713-2_8

Cited by 7 publications

(17 citation statements)

References 20 publications

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“…For example, [91] reviews agent-based cloud computing, [92] and [93] review the agent-based internet of things and cyber-physical systems and [94] reviews the applications of MAS in digital factories. By reviewing some of the applications proposed in the literature for MAS in these emerging technologies, one can con¯rm the vast potential of future work, such as smart vehicles and tra±c control systems [95][96][97][98], personalized medicine and healthcare systems [99][100][101], security and safety [102,103] and smart manufacturing [104,105]. This coupling can be attributed to the unique characteristics considered for agents and their capabilities for interaction.…”

Section: Discussionmentioning

confidence: 99%

Agent-Oriented Methodologies Evaluation Frameworks: A Review

Jazayeri

Bass

2020

Int. J. Soft. Eng. Knowl. Eng.

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Multi-agent systems and agent-oriented methodologies support analysis, characterization and development of complex software systems. These methodologies introduce different definitions for the essential components of multi-agent systems and cover different phases of the system development life cycle. Therefore, appropriate frameworks for evaluation and comparison of different methodologies would support developers to adopt the best methodology, or a combination of different methodologies, based on the project requirements. This review covers the system development phases and the main conceptual components in the context of multi-agent systems. Then, the evaluation frameworks proposed in the literature for comparison and evaluation of agent-oriented methodologies are reviewed. Evaluation frameworks proposed in the literature are categorized into three categories: methodology-based, phase-based and feature-based evaluation frameworks. The paper concludes with the agent-oriented methodologies’ usage challenges, their current limitations and potential future directions.

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Section: Discussionmentioning

confidence: 99%

Agent-Oriented Methodologies Evaluation Frameworks: A Review

Jazayeri

Bass

2020

Int. J. Soft. Eng. Knowl. Eng.

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“…Finally, the execution of behaviors is governed by the scheduler component, which may include different strategies [49] depending on the type of tasks and the constraints it may have. In particular, in this paper, we refer to real-time compliance to strict deadlines for task execution, as described in Section 4.…”

Section: An Agent-based Architecture For Decentralized Rdf Stream Promentioning

confidence: 99%

“…At t = 8.06 s, agent 2 negotiates the execution of the task τ5. According to the RBN protocol, the acceptance of a new task is subject to the schedulability test [49]. In this case, adding the τ5 to its task-set would bring its potential utilization factor to U pot = 0.55 (see black circle in Figure 12).…”

Section: Agent Id Need Id R W Tr Td N Mintmentioning

confidence: 99%

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Real-Time Compliant Stream Processing Agents for Physical Rehabilitation

Calvaresi

Calbimonte

2020

Sensors

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Digital rehabilitation is a novel concept that integrates state-of-the-art technologies for motion sensing and monitoring, with personalized patient-centric methodologies emerging from the field of physiotherapy. Thanks to the advances in wearable and portable sensing technologies, it is possible to provide patients with accurate monitoring devices, which simplifies the tracking of performance and effectiveness of physical exercises and treatments. Employing these approaches in everyday practice has enormous potential. Besides facilitating and improving the quality of care provided by physiotherapists, the usage of these technologies also promotes the personalization of treatments, thanks to data analytics and patient profiling (e.g., performance and behavior). However, achieving such goals implies tackling both technical and methodological challenges. In particular, (i) the capability of undertaking autonomous behaviors must comply with strict real-time constraints (e.g., scheduling, communication, and negotiation), (ii) plug-and-play sensors must seamlessly manage data and functional heterogeneity, and finally (iii) multi-device coordination must enable flexible and scalable sensor interactions. Beyond traditional top-down and best-effort solutions, unsuitable for safety-critical scenarios, we propose a novel approach for decentralized real-time compliant semantic agents. In particular, these agents can autonomously coordinate with each other, schedule sensing and data delivery tasks (complying with strict real-time constraints), while relying on ontology-based models to cope with data heterogeneity. Moreover, we present a model that represents sensors as autonomous agents able to schedule tasks and ensure interactions and negotiations compliant with strict timing constraints. Furthermore, to show the feasibility of the proposal, we present a practical study on upper and lower-limb digital rehabilitation scenarios, simulated on the MAXIM-GPRT environment for real-time compliance. Finally, we conduct an extensive evaluation of the implementation of the stream processing multi-agent architecture, which relies on existing RDF stream processing engines.

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“…http://hl7.org/fhir strategies[13], the integration of these data into semantic trajectories remains to be implemented.…”

mentioning

confidence: 99%

Agent-based Modeling for Ontology-driven Analysis of Patient Trajectories

2020

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Patients are often required to follow a medical treatment after discharge, e.g., for a chronic condition, rehabilitation after surgery, or for cancer survivor therapies. The need to adapt to new lifestyles, medication, and treatment routines, can produce an individual burden to the patient, who is often at home without the full support of healthcare professionals. Although technological solutions-in the form of mobile apps and wearables-have been proposed to mitigate these issues, it is essential to consider individual characteristics, preferences, and the context of a patient in order to offer personalized and effective support. The specific events and circumstances linked to an individual profile can be abstracted as a patient trajectory, which can contribute to a better understanding of the patient, her needs, and the most appropriate personalized support. Although patient trajectories have been studied for different illnesses and conditions, it remains challenging to effectively use them as the basis for data analytics methodologies in decentralized eHealth systems. In this work, we present a novel approach based on the multi-agent paradigm, considering patient trajectories as the cornerstone of a methodology for modelling eHealth support systems. In this design, semantic representations of individual treatment pathways are used in order to exchange patient-relevant information, potentially fed to AI systems for prediction and classification tasks. This paper describes the major challenges in this scope, as well as the design principles of the proposed agent-based architecture, including an example of its use through a case scenario for cancer survivors support. Keywords Patient trajectories • Semantic modeling • Agent-based modeling This article is part of the Topical Collection on Healthcare Intelligent Multi-Agent Systems (HIMAS2020)

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Local Scheduling in Multi-Agent Systems: Getting Ready for Safety-Critical Scenarios

Cited by 7 publications

References 20 publications

Agent-Oriented Methodologies Evaluation Frameworks: A Review

Agent-Oriented Methodologies Evaluation Frameworks: A Review

Real-Time Compliant Stream Processing Agents for Physical Rehabilitation

Agent-based Modeling for Ontology-driven Analysis of Patient Trajectories

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