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Cooperative multi-agents systems |  |
Conference papers
Agent-mediated application emergence through reinforcement learning from user feedback
Abstract : Cyber-physical and ambient systems surround the human user with applications that should be tailored as possible to her/his preferences and the current situation. We propose to build them automatically and on the fly by composition of software components present at the time in the environment, but without prior expression of the user’s needs or process specification or composition model. In order to produce knowledge useful for automatic composition in the absence of an initial guideline, we have developed a generic solution based on lifelong online reinforcement learning. It is decentralized within a multi-agent system where agents learn incrementally from user feedback to satisfy her/him. Different use cases have been experimented in which applications, adapted to the user and the situation, are composed and emerge automatically and continuously.
Keywords :
Agent
Ambient intelligence
Online learning paradigm
Reinforcement learning
Service discovery selection and composition
User feedback
Index Terms-Agent
multi-agent system
online learning
re- inforcement learning
user feedback
service discovery
selection and composition
ambient intelligence
Cited literature [23 references]
https://hal.archives-ouvertes.fr/hal-02895011
Contributor : Jean-Paul Arcangeli <>
Submitted on : Tuesday, November 3, 2020 - 11:40:53 AM
Last modification on : Friday, November 6, 2020 - 1:59:49 PM
Contributor : Jean-Paul Arcangeli <>
Submitted on : Tuesday, November 3, 2020 - 11:40:53 AM
Last modification on : Friday, November 6, 2020 - 1:59:49 PM