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Interactive Reinforcement Learning for Software Composition via Software Product Lines -Approach and Research Questions

Abstract : Opportunistic software composition of services is a novel interactive approach for the construction of software in open and dynamic ambient environments. The goal is to dynamically provide relevant applications to a user without predefined assembly plan or functional requirements. For that, an intelligent composition system builds, through distributed and interactive reinforcement learning, assemblies of software components present in the user's environment. A current limit of this approach is that in some situations, for example at startup, the composition engine lacks information and as a result proposes random assemblies to the user. The contribution discussed in this paper assists the engine in such situations by adding a feature model generated from the ambient environment. Thus, the engine gathers additional knowledge comparing its proposition to this feature model, providing more pertinent assemblies to the user.
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https://hal.archives-ouvertes.fr/hal-03600691
Contributor : Sylvie TROUILHET Connect in order to contact the contributor
Submitted on : Monday, March 7, 2022 - 5:14:24 PM
Last modification on : Monday, July 4, 2022 - 10:17:45 AM
Long-term archiving on: : Wednesday, June 8, 2022 - 8:53:20 PM

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IRIT_RR_2022_03_FR.pdf
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  • HAL Id : hal-03600691, version 1

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Kévin Delcourt, Françoise Adreit, Jean-Paul Arcangeli, Sylvie Trouilhet. Interactive Reinforcement Learning for Software Composition via Software Product Lines -Approach and Research Questions. [Research Report] IRIT--RR--2022--03--FR, IRIT : Institut de Recherche en Informatique de Toulouse, France. 2022, pp.1-6. ⟨hal-03600691⟩

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