Priority-based contextual local decision making in multi-agent systems

Barthès, Jean-Paul; Callebert, Lucile; Lourdeaux, Domitile

doi:10.1109/cscwd.2016.7565986

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…Tasks are then graded and the one with the top priority is chosen. Then while a task is being processed, priorities are recalculated at any time stage (tick) so that a task which has acquired a higher priority may interrupt the task if the latter is interruptible [32]. While the task is being processed in real time, the priority can be determined by the urgency of the task and its importance.…”

Section: Related Workmentioning

confidence: 99%

MAFC: Multi-Agent Fog Computing Model for Healthcare Critical Tasks Management

Mutlag

Ghani

Mohammed

et al. 2020

Sensors

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In healthcare applications, numerous sensors and devices produce massive amounts of data which are the focus of critical tasks. Their management at the edge of the network can be done by Fog computing implementation. However, Fog Nodes suffer from lake of resources That could limit the time needed for final outcome/analytics. Fog Nodes could perform just a small number of tasks. A difficult decision concerns which tasks will perform locally by Fog Nodes. Each node should select such tasks carefully based on the current contextual information, for example, tasks’ priority, resource load, and resource availability. We suggest in this paper a Multi-Agent Fog Computing model for healthcare critical tasks management. The main role of the multi-agent system is mapping between three decision tables to optimize scheduling the critical tasks by assigning tasks with their priority, load in the network, and network resource availability. The first step is to decide whether a critical task can be processed locally; otherwise, the second step involves the sophisticated selection of the most suitable neighbor Fog Node to allocate it. If no Fog Node is capable of processing the task throughout the network, it is then sent to the Cloud facing the highest latency. We test the proposed scheme thoroughly, demonstrating its applicability and optimality at the edge of the network using iFogSim simulator and UTeM clinic data.

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Section: Related Workmentioning

confidence: 99%

MAFC: Multi-Agent Fog Computing Model for Healthcare Critical Tasks Management

Mutlag

Ghani

Mohammed

et al. 2020

Sensors

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“…Priority-based constraint satisfaction relies on a suitable prioritization scheme. Barthès et al (2016) introduced a heuristic approach, which changes the priorities according to the circumstances and aims at mimicking human behavior. Static effects on the priority are distinguished from time-dependent ones and dependencies between tasks are considered.…”

Section: Introductionmentioning

confidence: 99%

Shared invariance control for constraint satisfaction in multi-robot systems

Kimmel

Pfort

Wöhlke

et al. 2019

The International Journal of Robotics Research

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In systems involving multiple intelligent agents, e.g. multi-robot systems, the satisfaction of environmental, inter-agent, and task constraints is essential to ensure safe and successful task execution. This requires a constraint enforcing control scheme, which is able to allocate and distribute the required evasive control actions adequately among the agents, ideally according to the role of the agents or the importance of the executed tasks. In this work, we propose a shared invariance control scheme in combination with a suitable agent prioritization to control multiple agents safely and reliably. Based on the projection of the constraints into the input spaces of the individual agents using input–output linearization, shared invariance control determines constraint enforcing control inputs and facilitates implementation in a distributed manner. In order to allow for shared evasive actions, the control approach introduces weighting factors derived from a two-stage prioritization scheme, which allots the weights according to a variety of factors such as a fixed task priority, the number of constraints affecting each agent or a manipulability measure. The proposed control scheme is proven to guarantee constraint satisfaction. The approach is illustrated in simulations and an experimental evaluation on a dual-arm robotic platform.

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Cognitive Agents and Ethical Behavior in Collaborative Teams

Barthès¹

2020

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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Priority-based contextual local decision making in multi-agent systems

Cited by 3 publications

References 5 publications

MAFC: Multi-Agent Fog Computing Model for Healthcare Critical Tasks Management

MAFC: Multi-Agent Fog Computing Model for Healthcare Critical Tasks Management

Shared invariance control for constraint satisfaction in multi-robot systems

Cognitive Agents and Ethical Behavior in Collaborative Teams

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