用大模型实现自然语言到网络策略的可靠转换，提前预测故障根源。
AI-driven Intent-Based Networking Approach for Self-configuration of Next Generation Networks
- 用大模型+结构化验证将模糊指令转为可执行策略
- 主动预测多意图场景下的故障并定位根本原因
- 提供可解释预警和修复准备时间，提升运维可信度
意图驱动网络（IBN）旨在通过将高层意图转化为可执行策略并保证合规性，简化异构基础设施的运维。然而，可靠自动化仍面临挑战：(i) 将模糊的自然语言意图转化为控制器可用策略存在脆弱性，易引发冲突和意外副作用；(ii) 保障机制常为被动响应，在多意图场景中故障导致级联症状，遥测数据模糊难辨。本文提出端到端闭环IBN流程，利用大语言模型结合结构化验证实现自然语言到策略的转化，并支持冲突感知激活；将保障机制重构为前瞻性多意图故障预测与根因消歧。预期成果是具备操作员信任的自动化系统，可提供可行动的早期预警、可解释的说明及可衡量的修复准备时长。
原文摘要 · Abstract (English)
Intent-Based Networking (IBN) aims to simplify operating heterogeneous infrastructures by translating high-level intents into enforceable policies and assuring compliance. However, dependable automation remains difficult because (i) realizing intents from ambiguous natural language into controller-ready policies is brittle and prone to conflicts and unintended side effects, and (ii) assurance is often reactive and struggles in multi-intent settings where faults create cascading symptoms and ambiguous telemetry. This paper proposes an end-to-end closed-loop IBN pipeline that uses large language models with structured validation for natural language to policy realization and conflict-aware activation, and reformulates assurance as proactive multi-intent failure prediction with root-cause disambiguation. The expected outcome is operator-trustworthy automation that provides actionable early warnings, interpretable explanations, and measurable lead time for remediation.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。