IGAA: Intent-Driven General Agentic AI for Edge Services Scheduling using Generative Meta Learning

2026-01-20

AI Summary

This work addresses the challenge of highly dynamic service demands in edge networks caused by user mobility, which hinders the generalization of existing scheduling agents to new scenarios. To overcome this limitation, the authors propose a meta-learning-based, intent-driven generalizable agent framework. The approach integrates a network-service-intent matrix for modeling, a transfer learning algorithm that incorporates causal reasoning and action potential awareness, and a generative intent replay mechanism to enable continual learning and efficient generalization. By leveraging large language models for enhanced reasoning and generative data synthesis, the method rapidly adapts scheduling policies to novel scenarios—such as low-latency vehicular services—achieving an intent satisfaction rate within 3.81% of scenario-specific methods. This significantly mitigates catastrophic forgetting and enhances cross-scenario adaptability.

Technology Category

• Planning, Routing, and Scheduling: Planning/Scheduling and Learning
• Multiagent Systems: Multiagent Learning
• Machine Learning: Learning on the Edge & Model Compression

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Agentic AI (AAI), which extends Large Language Models with enhanced reasoning capabilities, has emerged as a promising paradigm for autonomous edge service scheduling. However, user mobility creates highly dynamic service demands in edge networks, and existing service scheduling agents often lack generalization capabilities for new scenarios. Therefore, this paper proposes a novel Intent-Driven General Agentic AI (IGAA) framework. Leveraging a meta-learning paradigm, IGAA enables AAI to continuously learn from prior service scheduling experiences to achieve generalized scheduling capabilities. Particularly, IGAA incorporates three core mechanisms. First, we design a Network-Service-Intent matrix mapping method to allow agents to simulate novel scenarios and generate training datasets. Second, we present an easy-to-hard generalization learning scheme with two customized algorithms, namely Resource Causal Effect-aware Transfer Learning (RCETL) and Action Potential Optimality-aware Transfer Learning (APOTL). These algorithms help IGAA adapt to new scenarios. Furthermore, to prevent catastrophic forgetting during continual IGAA learning, we propose a Generative Intent Replay (GIR) mechanism that synthesizes historical service data to consolidate prior capabilities. Finally, to mitigate the effect of LLM hallucinations on scenario simulation, we incorporate a scenario evaluation and correction model to guide agents in generating rational scenarios and datasets. Extensive experiments demonstrate IGAA's strong generalization and scalability. Specifically, IGAA enables rapid adaptation by transferring learned policies to analogous new ones, such as applying latency-sensitive patterns from real-time computing to optimize novel Internet of Vehicles (IoV) services. Compared to scenario-specific methods, IGAA maintains the intent-satisfaction rate gap within 3.81%.

Problem

• Edge Services Scheduling
• User Mobility
• Generalization
• Agentic AI
• Dynamic Service Demand

Innovation

• Agentic AI
• Meta-Learning
• Edge Service Scheduling
• Intent-Driven Generalization
• Generative Replay