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Found 12 papers, 2 papers with code
Statistical and Machine Learning Models for Predicting Fire and Other Emergency Events
Accurate and timely prediction of events can help the emergency fire and rescue services in preparing for and mitigating the consequences of emergency events.
Intent Assurance using LLMs guided by Intent Drift
Intent-Based Networking (IBN) presents a paradigm shift for network management, by promising to align intents and business objectives with network operations--in an automated manner.
LLM-based policy generation for intent-based management of applications
And the task of identifying and adapting these steps (as conditions change) requires a decomposition approach that cannot be exactly pre-defined beforehand.
Missing Data Estimation in Temporal Multilayer Position-aware Graph Neural Network (TMP-GNN)
GNNs have been proven to perform highly effective in various node-level, edge-level, and graph-level prediction tasks in several domains.
A Deep Reinforcement Learning Approach for Fair Traffic Signal Control
Traffic signal control is one of the most effective methods of traffic management in urban areas.
Toward Efficient Transfer Learning in 6G
We also provide a list of future research directions in TL for 6G.
Queue-Learning: A Reinforcement Learning Approach for Providing Quality of Service
End-to-end delay is a critical attribute of quality of service (QoS) in application domains such as cloud computing and computer networks.
Estimation of Missing Data in Intelligent Transportation System
We evaluate the effectiveness of this approach on a TomTom dataset containing spatio-temporal measurements of average vehicle speed and travel time in the Greater Toronto Area (GTA).
Reinforcement Learning-based Admission Control in Delay-sensitive Service Systems
Our evaluations verify that the proposed RL-based admission controller is capable of providing probabilistic bounds on the end-to-end delay of the network, without using system model information.
Probabilistic Bounds on the End-to-End Delay of Service Function Chains using Deep MDN
Ensuring the conformance of a service system's end-to-end delay to service level agreement (SLA) constraints is a challenging task that requires statistical measures beyond the average delay.
Performance Networking and Internet Architecture
On the Robustness of Cooperative Multi-Agent Reinforcement Learning
Our results on the StartCraft II multi-agent benchmark demonstrate that c-MARL teams are highly vulnerable to perturbations applied to one of their agent's observations.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Artificial Intelligence as a Services (AI-aaS) on Software-Defined Infrastructure
This paper investigates a paradigm for offering artificial intelligence as a service (AI-aaS) on software-defined infrastructures (SDIs).
