Context:
Smart and adaptive Systems, such as self-adaptive and self-organising (SASO) systems, typically consist of a large set of highly autonomous and heterogeneous subsystems that are able to adapt their behaviour to the requirements of ever-changing, dynamic environments. Their successful operation is based on appropriate modelling of the internal and external conditions.
Objective:
The control problem for establishing a near-to-optimal coordinated behaviour of systems with multiple, potentially conflicting objectives is either approached in a distributed (i.e., fully autonomous by the autonomous subsystems) or in a centralised way (i.e. one instance controlling the optimisation and planning process). In the distributed approach, selfish behaviour and being limited to local knowledge may lead to sub-optimal system behaviour, while the centralised approach ignores the autonomy and the coordination efforts of parts of the system.
Method:
In this article, we present a concept for a hybrid (i.e., integrating a central optimisation with a distributed decision-making process) system management that combines local reinforcement learning and self-awareness mechanisms of fully autonomous subsystems with external system-wide planning and optimisation of adaptation freedom that steers the behaviour dynamically by issuing plans and guidelines augmented with incentivisation schemes.
Results:
This work addresses the inherent uncertainty of the dynamic system behaviour, the local autonomous and context-aware learning of subsystems, and proactive control based on adaptiveness. We provide the ‘swarm-fleet infrastructure’—a self-organised taxi service established by autonomous, privately-owned cars—as a testbed for structured comparison of systems.
Conclusion:
The ‘swarm-fleet infrastructure’ supports the advantages of a proactive hybrid self-adaptive and self-organising system operation. Further, we provide a system model to combine the system-wide optimisation while ensuring local decision-making through reinforcement learning for individualised configurations.
%0 Journal Article
%1 krupitzer2022proactive
%A Krupitzer, Christian
%A Gruhl, Christian
%A Sick, Bernhard
%A Tomforde, Sven
%D 2022
%I Elsevier
%J Information and Software Technology
%K imported itegpub isac-www
%P 106826
%R 10.1016/j.infsof.2022.106826
%T Proactive hybrid learning and optimisation in self-adaptive systems: The swarm-fleet infrastructure scenario
%U https://www.sciencedirect.com/science/article/abs/pii/S0950584922000052
%V 145
%X Context:
Smart and adaptive Systems, such as self-adaptive and self-organising (SASO) systems, typically consist of a large set of highly autonomous and heterogeneous subsystems that are able to adapt their behaviour to the requirements of ever-changing, dynamic environments. Their successful operation is based on appropriate modelling of the internal and external conditions.
Objective:
The control problem for establishing a near-to-optimal coordinated behaviour of systems with multiple, potentially conflicting objectives is either approached in a distributed (i.e., fully autonomous by the autonomous subsystems) or in a centralised way (i.e. one instance controlling the optimisation and planning process). In the distributed approach, selfish behaviour and being limited to local knowledge may lead to sub-optimal system behaviour, while the centralised approach ignores the autonomy and the coordination efforts of parts of the system.
Method:
In this article, we present a concept for a hybrid (i.e., integrating a central optimisation with a distributed decision-making process) system management that combines local reinforcement learning and self-awareness mechanisms of fully autonomous subsystems with external system-wide planning and optimisation of adaptation freedom that steers the behaviour dynamically by issuing plans and guidelines augmented with incentivisation schemes.
Results:
This work addresses the inherent uncertainty of the dynamic system behaviour, the local autonomous and context-aware learning of subsystems, and proactive control based on adaptiveness. We provide the ‘swarm-fleet infrastructure’—a self-organised taxi service established by autonomous, privately-owned cars—as a testbed for structured comparison of systems.
Conclusion:
The ‘swarm-fleet infrastructure’ supports the advantages of a proactive hybrid self-adaptive and self-organising system operation. Further, we provide a system model to combine the system-wide optimisation while ensuring local decision-making through reinforcement learning for individualised configurations.
@article{krupitzer2022proactive,
abstract = { Context:
Smart and adaptive Systems, such as self-adaptive and self-organising (SASO) systems, typically consist of a large set of highly autonomous and heterogeneous subsystems that are able to adapt their behaviour to the requirements of ever-changing, dynamic environments. Their successful operation is based on appropriate modelling of the internal and external conditions.
Objective:
The control problem for establishing a near-to-optimal coordinated behaviour of systems with multiple, potentially conflicting objectives is either approached in a distributed (i.e., fully autonomous by the autonomous subsystems) or in a centralised way (i.e. one instance controlling the optimisation and planning process). In the distributed approach, selfish behaviour and being limited to local knowledge may lead to sub-optimal system behaviour, while the centralised approach ignores the autonomy and the coordination efforts of parts of the system.
Method:
In this article, we present a concept for a hybrid (i.e., integrating a central optimisation with a distributed decision-making process) system management that combines local reinforcement learning and self-awareness mechanisms of fully autonomous subsystems with external system-wide planning and optimisation of adaptation freedom that steers the behaviour dynamically by issuing plans and guidelines augmented with incentivisation schemes.
Results:
This work addresses the inherent uncertainty of the dynamic system behaviour, the local autonomous and context-aware learning of subsystems, and proactive control based on adaptiveness. We provide the ‘swarm-fleet infrastructure’—a self-organised taxi service established by autonomous, privately-owned cars—as a testbed for structured comparison of systems.
Conclusion:
The ‘swarm-fleet infrastructure’ supports the advantages of a proactive hybrid self-adaptive and self-organising system operation. Further, we provide a system model to combine the system-wide optimisation while ensuring local decision-making through reinforcement learning for individualised configurations.},
added-at = {2022-02-16T12:40:41.000+0100},
author = {Krupitzer, Christian and Gruhl, Christian and Sick, Bernhard and Tomforde, Sven},
biburl = {https://www.bibsonomy.org/bibtex/2b34f9014cc2a9f791aef2e40a6f43dd8/ies},
doi = {10.1016/j.infsof.2022.106826},
interhash = {36854b5fe7e895d3a4ccc4fe11a3fc84},
intrahash = {b34f9014cc2a9f791aef2e40a6f43dd8},
journal = {Information and Software Technology},
keywords = {imported itegpub isac-www},
pages = 106826,
publisher = {Elsevier},
timestamp = {2022-02-16T12:40:41.000+0100},
title = {Proactive hybrid learning and optimisation in self-adaptive systems: The swarm-fleet infrastructure scenario},
url = {https://www.sciencedirect.com/science/article/abs/pii/S0950584922000052},
volume = 145,
year = 2022
}