  This paper addresses the problem of enabling inter-machine Ultra-Reliable
Low-Latency Communication (URLLC) in future 6G Industrial Internet of Things
(IIoT) networks. As far as the Radio Access Network (RAN) is concerned,
centralized pre-configured resource allocation requires scheduling grants to be
disseminated to the User Equipments (UEs) before uplink transmissions, which is
not efficient for URLLC, especially in case of flexible/unpredictable traffic.
To alleviate this burden, we study a distributed, user-centric scheme based on
machine learning in which UEs autonomously select their uplink radio resources
without the need to wait for scheduling grants or preconfiguration of
connections. Using simulation, we demonstrate that a Multi-Armed Bandit (MAB)
approach represents a desirable solution to allocate resources with URLLC in
mind in an IIoT environment, in case of both periodic and aperiodic traffic,
even considering highly populated networks and aggressive traffic.
