基于人工智能的意图驱动网络方法用于下一代网络的自配置
AI-driven Intent-Based Networking Approach for Self-configuration of Next Generation Networks
AI总结：
本文提出一种端到端闭环IBN流程，利用大语言模型和结构化验证将自然语言意图转化为可执行策略，并通过多意图故障预测提升自动化可靠性。
摘要：
意图驱动网络（IBN）旨在通过将高层意图转化为可执行策略和保证合规性来简化异构基础设施的操作。然而，可靠的自动化仍具挑战性，因为（i）将模糊的自然语言意图转化为控制器准备的策略过程脆弱且容易产生冲突和意外副作用，（ii）保证通常是反应式的，在多意图设置中难以处理故障引发的连锁症状和模糊遥测。本文提出了一种端到端闭环IBN流程，利用大语言模型和结构化验证进行自然语言到策略的实现和冲突感知激活，并将保证重新表述为主动的多意图故障预测与根本原因澄清。预期的结果是运营商可信赖的自动化，提供可操作的早期警告、可解释的解释和可衡量的修复时间提前量。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
