Quality-Based Reinforcement Learning in Intelligent Opportunistic Software Composition
Résumé
Internet of Things and cyber-physical systems are characterised by openness and an increasing number of devices and their associated services.
In a previous work, we have proposed to exploit opportunistically these services in order to automatically make emerge customised applications that suit user preferences.
For that, we have developed a generic solution for bottom-up opportunistic service composition, based on reinforcement learning.
In this work, it is extended to handle more efficiently the appearance of new components using \textit{service annotation} and \textit{quality attributes} in order to generalise and share knowledge with new discovered services.
A didactic use case is used for illustration and demonstration purposes.
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