Adaptive routing protocols for determining optimal paths in AI multi-agent systems: a priority- and learning-enhanced approach

• URL: http://arxiv.org/abs/2503.07686v1
• Date: Mon, 10 Mar 2025 13:16:54 GMT
• Title: Adaptive routing protocols for determining optimal paths in AI multi-agent systems: a priority- and learning-enhanced approach
• Authors: Theodor Panayotov, Ivo Emanuilov,
• Abstract summary: This paper introduces an enhanced, adaptive routing tailored for AI multi-agent networks.<n>We incorporate multi-faceted parameters such as task complexity, user request priority, agent capabilities, bandwidth, latency, load, model sophistication, and reliability.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: As distributed artificial intelligence (AI) and multi-agent architectures grow increasingly complex, the need for adaptive, context-aware routing becomes paramount. This paper introduces an enhanced, adaptive routing algorithm tailored for AI multi-agent networks, integrating priority-based cost functions and dynamic learning mechanisms. Building on an extended Dijkstra-based framework, we incorporate multi-faceted parameters such as task complexity, user request priority, agent capabilities, bandwidth, latency, load, model sophistication, and reliability. We further propose dynamically adaptive weighting factors, tuned via reinforcement learning (RL), to continuously evolve routing policies based on observed network performance. Additionally, heuristic filtering and hierarchical routing structures improve scalability and responsiveness. Our approach yields context-sensitive, load-aware, and priority-focused routing decisions that not only reduce latency for critical tasks but also optimize overall resource utilization, ultimately enhancing the robustness, flexibility, and efficiency of multi-agent systems.

Related papers

• UserCentrix: An Agentic Memory-augmented AI Framework for Smart Spaces [8.111700384985356]
  Agentic AI, with its autonomous and proactive decision-making, has transformed smart environments. This paper introduces UserCentrix, an agentic memory-augmented AI framework designed to enhance smart spaces through dynamic, context-aware decision-making.
  arXiv  Detail & Related papers  (2025-05-01T11:54:49Z)
• Accelerating Vehicle Routing via AI-Initialized Genetic Algorithms [55.78505925402658]
  Vehicle Routing Problems (VRP) are an extension of the Traveling Salesperson Problem and are a fundamental NP-hard challenge in Evolutionary optimization. We introduce a novel optimization framework that uses a reinforcement learning agent - trained on prior instances - to quickly generate initial solutions, which are then further optimized by genetic algorithms. For example, EARLI handles vehicle routing with 500 locations within 1s, 10x faster than current solvers for the same solution quality, enabling applications like real-time and interactive routing.
  arXiv  Detail & Related papers  (2025-04-08T15:21:01Z)
• Design Optimization of NOMA Aided Multi-STAR-RIS for Indoor Environments: A Convex Approximation Imitated Reinforcement Learning Approach [51.63921041249406]
  Non-orthogonal multiple access (NOMA) enables multiple users to share the same frequency band, and simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) deploying STAR-RIS indoors presents challenges in interference mitigation, power consumption, and real-time configuration. A novel network architecture utilizing multiple access points (APs), STAR-RISs, and NOMA is proposed for indoor communication.
  arXiv  Detail & Related papers  (2024-06-19T07:17:04Z)
• A Deep Reinforcement Learning Approach for Adaptive Traffic Routing in Next-gen Networks [1.1586742546971471]
  Next-gen networks require automation and adaptively adjust network configuration based on traffic dynamics. Traditional techniques that decide traffic policies are usually based on hand-crafted programming optimization and algorithms. We develop a deep reinforcement learning (DRL) approach for adaptive traffic routing.
  arXiv  Detail & Related papers  (2024-02-07T01:48:29Z)
• Joint User Association, Interference Cancellation and Power Control for Multi-IRS Assisted UAV Communications [80.35959154762381]
  Intelligent reflecting surface (IRS)-assisted unmanned aerial vehicle (UAV) communications are expected to alleviate the load of ground base stations in a cost-effective way. Existing studies mainly focus on the deployment and resource allocation of a single IRS instead of multiple IRSs. We propose a new optimization algorithm for joint IRS-user association, trajectory optimization of UAVs, successive interference cancellation (SIC) decoding order scheduling and power allocation.
  arXiv  Detail & Related papers  (2023-12-08T01:57:10Z)
• Multi-Agent Reinforcement Learning for Power Control in Wireless Networks via Adaptive Graphs [1.1861167902268832]
  Multi-agent deep reinforcement learning (MADRL) has emerged as a promising method to address a wide range of complex optimization problems like power control. We present the use of graphs as communication-inducing structures among distributed agents as an effective means to mitigate these challenges.
  arXiv  Detail & Related papers  (2023-11-27T14:25:40Z)
• Federated Multi-Level Optimization over Decentralized Networks [55.776919718214224]
  We study the problem of distributed multi-level optimization over a network, where agents can only communicate with their immediate neighbors. We propose a novel gossip-based distributed multi-level optimization algorithm that enables networked agents to solve optimization problems at different levels in a single timescale. Our algorithm achieves optimal sample complexity, scaling linearly with the network size, and demonstrates state-of-the-art performance on various applications.
  arXiv  Detail & Related papers  (2023-10-10T00:21:10Z)
• Design Principles for Model Generalization and Scalable AI Integration in Radio Access Networks [2.846642778157227]
  This paper emphasizes the pivotal role of achieving model generalization in enhancing performance and enabling scalable AI integration within radio communications. We outline design principles for model generalization in three key domains: environment for robustness, intents for adaptability to system objectives, and control tasks for reducing AI-driven control loops. We propose a learning architecture that leverages centralization of training and data management functionalities, combined with distributed data generation.
  arXiv  Detail & Related papers  (2023-06-09T20:46:31Z)
• Network-Aided Intelligent Traffic Steering in 6G O-RAN: A Multi-Layer Optimization Framework [47.57576667752444]
  We jointly optimize the flow-split distribution, congestion control and scheduling (JFCS) to enable an intelligent steering application in open RAN (O-RAN) Our main contributions are three-fold: i) we propose the novel JFCS framework to efficiently and adaptively direct traffic to appropriate radio units; ii) we develop low-complexity algorithms based on the reinforcement learning, inner approximation and bisection search methods to effectively solve the JFCS problem in different time scales; and iv) the rigorous theoretical performance results are analyzed to show that there exists a scaling factor to improve the tradeoff between delay and utility-optimization
  arXiv  Detail & Related papers  (2023-02-06T11:37:06Z)
• Hierarchical Multi-Agent DRL-Based Framework for Joint Multi-RAT Assignment and Dynamic Resource Allocation in Next-Generation HetNets [21.637440368520487]
  This paper considers the problem of cost-aware downlink sum-rate via joint optimal radio access technologies (RATs) assignment and power allocation in next-generation wireless networks (HetNets) We propose a hierarchical multi-agent deep reinforcement learning (DRL) framework, called DeepRAT, to solve it efficiently and learn system dynamics. In particular, the DeepRAT framework decomposes the problem into two main stages; the RATs-EDs assignment stage, which implements a single-agent Deep Q Network algorithm, and the power allocation stage, which utilizes a multi-agent Deep Deterministic Policy Gradient
  arXiv  Detail & Related papers  (2022-02-28T09:49:44Z)
• Reconfigurable Intelligent Surface Assisted Mobile Edge Computing with Heterogeneous Learning Tasks [53.1636151439562]
  Mobile edge computing (MEC) provides a natural platform for AI applications. We present an infrastructure to perform machine learning tasks at an MEC with the assistance of a reconfigurable intelligent surface (RIS) Specifically, we minimize the learning error of all participating users by jointly optimizing transmit power of mobile users, beamforming vectors of the base station, and the phase-shift matrix of the RIS.
  arXiv  Detail & Related papers  (2020-12-25T07:08:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.