Search Results for author:
Found 7 papers, 0 papers with code
A Survey on the Integration of Generative AI for Critical Thinking in Mobile Networks
Specifically, the aim of this study is to offer an introduction to the potential utilization of GenAI for critical thinking techniques in mobile networks, while also establishing a foundation for future research.
Multi-agent transformer-accelerated RL for satisfaction of STL specifications
One of the main challenges in multi-agent reinforcement learning is scalability as the number of agents increases.
Safe RAN control: A Symbolic Reinforcement Learning Approach
In particular, we provide a purely automated procedure in which a user can specify high-level logical safety specifications for a given cellular network topology in order for the latter to execute optimal safe performance which is measured through certain Key Performance Indicators (KPIs).
Distributed Learning in Wireless Networks: Recent Progress and Future Challenges
Then, we present a detailed literature review on the use of communication techniques for its efficient deployment.
Symbolic Reinforcement Learning for Safe RAN Control
In this paper, we demonstrate a Symbolic Reinforcement Learning (SRL) architecture for safe control in Radio Access Network (RAN) applications.
Machine Reasoning Explainability
As a field of AI, Machine Reasoning (MR) uses largely symbolic means to formalize and emulate abstract reasoning.
A Framework for Knowledge Management and Automated Reasoning Applied on Intelligent Transport Systems
Cyber-Physical Systems in general, and Intelligent Transport Systems (ITS) in particular use heterogeneous data sources combined with problem solving expertise in order to make critical decisions that may lead to some form of actions e. g., driver notifications, change of traffic light signals and braking to prevent an accident.
