Internet Research Task Force                                  M. Gu, Ed.Internet-Draft                                             J. Jeong, Ed.Intended status: Informational                   Sungkyunkwan UniversityExpires: 23 April 2026                                   20 October 2025         An Intent Translation Framework for Internet of Things                   draft-gu-nmrg-intent-translator-02Abstract   The evolution of 6G networks and the expansion of Internet of Things   (IoT) environments introduce new challenges in managing diverse   networked resources.  Intent-based management frameworks enable   operators to express desired network outcomes using high-level   intents, often articulated in natural language.  However, converting   these expressions into machine-executable policy configurations   remains an open challenge.   This document defines an intent translation framework designed to   bridge the gap between user-issued intents and structured policy   representations for 6G enabled IoT systems.  The framework accepts   natural language intent as input and produces a policy document in a   structured format, such as YAML, that aligns with the intent model   defined in 3GPP in [TS-28.312].   The framework consists of modular components responsible for   processing input, aligning user intent with domain knowledge,   evaluating semantic confidence, and generating standardized output.   This modularity supports transparency, interoperability, and   automated policy enforcement in next-generation network   infrastructures.Status of This Memo   This Internet-Draft is submitted in full conformance with the   provisions of BCP 78 and BCP 79.   Internet-Drafts are working documents of the Internet Engineering   Task Force (IETF).  Note that other groups may also distribute   working documents as Internet-Drafts.  The list of current Internet-   Drafts is at https://datatracker.ietf.org/drafts/current/.   Internet-Drafts are draft documents valid for a maximum of six months   and may be updated, replaced, or obsoleted by other documents at any   time.  It is inappropriate to use Internet-Drafts as reference   material or to cite them other than as "work in progress."Gu & Jeong                Expires 23 April 2026                 [Page 1]Internet-Draft         Intent Translation for IoT           October 2025   This Internet-Draft will expire on 23 April 2026.Copyright Notice   Copyright (c) 2025 IETF Trust and the persons identified as the   document authors.  All rights reserved.   This document is subject to BCP 78 and the IETF Trust's Legal   Provisions Relating to IETF Documents (https://trustee.ietf.org/   license-info) in effect on the date of publication of this document.   Please review these documents carefully, as they describe your rights   and restrictions with respect to this document.  Code Components   extracted from this document must include Revised BSD License text as   described in Section 4.e of the Trust Legal Provisions and are   provided without warranty as described in the Revised BSD License.Table of Contents   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2   2.  Terminology . . . . . . . . . . . . . . . . . . . . . . . . .   3   3.  Intent Translator Framework Architecture  . . . . . . . . . .   4     3.1.  Intent Translator . . . . . . . . . . . . . . . . . . . .   5     3.2.  Semantic Mapper . . . . . . . . . . . . . . . . . . . . .   7     3.3.  Intent Resolver . . . . . . . . . . . . . . . . . . . . .   9   4.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  11   5.  Deployment Considerations . . . . . . . . . . . . . . . . . .  11   6.  Extensibility Considerations  . . . . . . . . . . . . . . . .  11   7.  Degradation and Human Oversight Considerations  . . . . . . .  12   8.  Security Considerations . . . . . . . . . . . . . . . . . . .  12   9.  References  . . . . . . . . . . . . . . . . . . . . . . . . .  12     9.1.  Normative References  . . . . . . . . . . . . . . . . . .  12     9.2.  Informative References  . . . . . . . . . . . . . . . . .  13   Appendix A.  Acknowledgments  . . . . . . . . . . . . . . . . . .  16   Appendix B.  Contributors . . . . . . . . . . . . . . . . . . . .  16   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  161.  Introduction   The rapid growth of Internet of Things (IoT) deployments and the   evolution toward 6G networks have introduced increasing complexity in   the management of heterogeneous devices, services, and application   policies.  As operational environments scale, the traditional model   of manually configuring service-level policies becomes unsustainable.   Intent-based management is a paradigm that allows administrators to   specify desired outcomes through high-level intents, often expressed   in natural language.  These intents must then be interpreted,   validated, and translated into structured policy representations thatGu & Jeong                Expires 23 April 2026                 [Page 2]Internet-Draft         Intent Translation for IoT           October 2025   can be executed by network functions and orchestrators.  While   standards such as 3GPP TS 28.312 define lifecycle procedures for   managing intents in mobile networks, they do not specify mechanisms   for interpreting natural language or translating it into compliant   policy structures.   This document defines a modular intent translation framework that   addresses this gap.  The framework enables automated conversion of   user-issued intents into structured policy outputs in formats such as   YAML, aligned with the expectations and procedures defined in 3GPP TS   28.312.  It supports a range of use cases across IoT domains,   including resource optimization, security management, and service   quality assurance.   The framework is composed of functional components that operate   sequentially or in coordination:   *  Intent Processing Component:Accepts and interprets user-provided      intents into structured representations.   *  Semantic Alignment Component:Matches the processed intent to      relevant domain knowledge for policy resolution.   *  Confidence Evaluation Component:Assesses interpretation      reliability and identifies degraded or low-confidence mappings.   *  Policy Generation Component:Produces a machine-readable policy in      a structured format suitable for deployment.   The design promotes modularity, transparency, and alignment with   existing network automation architectures.  It enables consistent   translation of operator goals into policies that are interoperable   with standard orchestration and management systems in IoT   environment.2.  Terminology   This document uses the terminology defined in [RFC9315], [RFC8329],   [I-D.ietf-i2nsf-applicability],   [I-D.jeong-nmrg-security-management-automation], and [SPT].   In addition, the following terms are defined for the purpose of this   document:   *  Intent: A set of operational goals (that a network should meet)      and outcomes (that a network is supposed to deliver), defined in a      declarative manner without specifying how to achieve or implement      them [RFC9315].Gu & Jeong                Expires 23 April 2026                 [Page 3]Internet-Draft         Intent Translation for IoT           October 2025   *  Intent-Based Management (IBM): It enforces an intent from a user      (or administrator) into a target system.  An intent can be      expressed as a natural language sentence and translated into a      high-level policy using natural language interpretation and      structured policy mapping      [I-D.jeong-nmrg-security-management-automation], [SPT].  This      high-level policy is then transformed into low-level policies that      are dispatched to appropriate Service Functions (SFs).  Based on      monitoring feedback, new rules may be generated or existing      policies refined.   *  Intent Processing Component: A logical function that receives a      natural language input from the user or operator and produces a      structured internal representation of the intent.  This component      facilitates the initial abstraction required for intent lifecycle      operations [RFC9315],      [I-D.jeong-nmrg-security-management-automation].   *  Semantic Alignment Component: A logical function that interprets      the structured intent and determines its best alignment with      existing domain knowledge or policy databases.  The purpose of      this component is to ensure that the intent maps to a resolvable      and enforceable policy outcome.   *  Confidence Evaluation Component: A logical function that estimates      the reliability or confidence of semantic intent translation.      Low-confidence outputs may be flagged as degraded and subjected to      fallback, verification, or reprocessing.   *  Degraded Intent: An intent translation result that has been marked      as low-confidence due to weak alignment, missing information, or      uncertainty in the reasoning process.  A degraded intent may still      result in policy generation, but with warnings or limited scope.   *  Policy Generation Component: A logical function that produces a      machine-readable policy, typically in a format such as YAML or      JSON, based on the resolved intent and domain mappings.  This      component ensures compliance with policy schema requirements, such      as those defined in 3GPP [TS-28.312].3.  Intent Translator Framework Architecture   This section defines the architecture of the Intent Translator   Framework, which is designed to convert high-level, natural language   intents into machine-readable policy representations in structured   formats such as YAML.  The framework enables intent-based management   automation for IoT environments and aligns with policy modeling and   lifecycle procedures defined in [TS-28.312].Gu & Jeong                Expires 23 April 2026                 [Page 4]Internet-Draft         Intent Translation for IoT           October 20253.1.  Intent Translator   The Intent Translator is a modular subsystem responsible for   converting natural language intent into a structured, machine-   readable policy document suitable for enforcement in intent-based   management systems.  It forms the core of the intent translation   framework, providing semantic interpretation, policy grounding, and   output generation.  The final output is expressed in a structured   format such as YAML and adheres to policy modeling defined in   [TS-28.312].   Architecturally, the Intent Translator operates as a sequential   pipeline composed of six logically distinct components.  Each   component handles a specific transformation step, beginning with user   input and ending with policy document generation.  The pipeline   enables soft semantic matching, confidence scoring, and graceful   handling of degraded translations.  It is designed to support   transparent, extensible, and standards-aligned translation of human   goals into actionable configurations.        +----------+        | IBN User |        +----------+              |              | +------------+------------------------------------------------------------+ |            v                                          Intent Translator | |  +--------------------+   +------------------+                          |<+--| Intent Coordinator |-->| Intent Extractor |                          | |  +--------------------+   +------------------+                          | |            ^                       |                                    | |            |                       v                                    | |            |              +-----------------+                           | |            |              | Semantic Mapper |                           | |            |              +-----------------+                           | |            |                       |                                    | |            |                       v                                    | |  +-----------------+      +-----------------+       +----------------+  | |  | Policy Composer |<-----| Intent Resolver |<----->| Policy         |  | |  +-----------------+      +-----------------+       | Knowledge Base |  | |                                                     +----------------+  | +-------------------------------------------------------------------------+          Figure 1: Intent Translator Component ArchitectureGu & Jeong                Expires 23 April 2026                 [Page 5]Internet-Draft         Intent Translation for IoT           October 2025   The six main components of the Intent Translator are illustrated in   Figure 1.   *  Intent Coordinator: An initial component of Intent Translator that      receives user intent; dispatches and deploys.  This component      receives a natural language intent submitted by an IBN user or      administrative interface.  It forwards the natural language intent      to the Intent Extractor.  On the other hand, once the Policy      Composer generates the final structured policy, the Intent      Coordinator is also responsible for delivering it to downstream      systems for enforcement, such as network controllers or      orchestration engines.   *  Intent Extractor: A Few shot-based Large Language Model (LLM)      informed component that extracts structured elements from anatural      language [Flan-T5][GPT-3].  This component parses the incoming      natural language statement to extract key semantic elements such      as action, expactation object, and expactation target.  These      elements form the core of the structured intent representation and      are passed to the Semantic mapper for KG embedding and semantic      alignment.   *  Semantic Mapper: A component that projects structured intent into      a semantic space aligned with domain knowledge.  This component      maps the structured intent fields into dense vector      representations using a pre-trained embedding space.  The      embedding model is aligned with a domain-specific knowledge graph      and allows the intent representation to be projected into the same      semantic space used for stored policy facts.  The aggregated      intent vector is delivered to the reasoning module, Intent      Resolver.   *  Intent Resolver: A Reasoning module that atches intent to policy      triples; flags degraded matches.  This component receives the      embedded intent vector and compares it against the embeddings of      knowledge graph triples stored in the Policy Knowledge Base.  If a      direct match is not available, soft matching is performed using      semantic similarity scoring (e.g., cosine distance).  When the      similarity score for the best match falls below a defined      threshold, the match is flagged as degraded.  This degraded status      is propagated downstream, enabling conditional processing and      transparency in policy output.   *  Policy Knowledge Base: A knowledge base component that stores      domain knowledge to provides embeddings and semantic structure.      The Knowledge Base maintains the structured knowledge graph used      throughout the translation process.  It contains entity-relation      triples that define valid intent mappings and serviceGu & Jeong                Expires 23 April 2026                 [Page 6]Internet-Draft         Intent Translation for IoT           October 2025      configurations.  During reasoning, the Intent Resolver retrieves      and compares Knowledge Base entries based on their semantic      proximity to the intent vector.  The Policy Knowledge Base      supports approximate matching during inference.   *  Policy Composer: Generates YAML-formatted policy documents for      deployment.  The final component of the translation pipeline is      responsible for synthesizing a policy document that aligns with      [TS-28.312].  It uses both the extracted intent structure and the      selected knowledge graph entry to construct a template based YAML-      formatted policy.   Together, these components enable the reliable and transparent   transformation of user-defined goals into system-aligned, deployable   policies.  The architecture is modular and extensible, allowing   domain-specific enhancements without modification to the full   translation pipeline.3.2.  Semantic Mapper   The Semantic Mapper translates structured intent fields into a   unified semantic representation within an embedding space aligned   with the Policy Knowledge Base.  It serves as a semantic abstraction   layer that enables approximate intent matching through latent space   projection.Gu & Jeong                Expires 23 April 2026                 [Page 7]Internet-Draft         Intent Translation for IoT           October 2025               +------------------+               | Intent Extractor |               +------------------+                         |     +-------------------+-------------------+     |                   |          Semantic |     |                   v            Mapper |     |    +-----------------------------+    |     |    |       Embedding Module      |    |     |    |  (Per-slot Vector Encoding) |    |     |    +-----------------------------+    |     |                   |                   |     |                   v                   |     |    +-----------------------------+    |     |    |         Aggregator          |    |     |    | (Intent Embedding Builder)  |    |     |    +-----------------------------+    |     |                   |                   |     |                   v                   |     |     [ Aggregated Intent Vector ]      |     +-------------------+-------------------+                         v                +-----------------+          +----------------+                | Intent Resolver |<-------->| Knowledge Base |                +-----------------+          +----------------+                Figure 2: Semantic Mapper Internal Workflow   Figure 2 illustrates the internal flow of the Semantic Mapper.  It   begins with a structured intent delivered from the Intent Parser.   The input undergoes semantic enrichment using a few-shot language   model, followed by vector encoding of each semantic slot.  The final   stage aggregates the slot-wise vectors into a single intent embedding   vector, which is then forwarded to the Intent Resolver.   The internal components of the Semantic Mapper are illustrated in   Figure 2.   *  Embedding Module: A component that supports Per-slot Vector      Encoding.  Each enriched intent slots (e.g., action, expaction      object, expectation target) is independently encoded as a dense      vector in a shared semantic space.  This allows for flexible      alignment across synonymous or paraphrased expressions.   *  Aggregator: A component that builds intent embeddings.  This      component uses a simple neural network to learn weights for      individual slots, dynamically reflecting the importance of eachGu & Jeong                Expires 23 April 2026                 [Page 8]Internet-Draft         Intent Translation for IoT           October 2025      slot to construct an intent vector, and aggregates it into a      single composite intent embedding.  This intent embedding vector      serves as a holistic semantic representation of the user's goal,      facilitating efficient semantic comparisons during the inference      phase.   The final output of the Semantic Mapper is the aggregated intent   vector, which is passed to the Intent Resolver for policy resolution.   This design supports soft matching and generalization over variations   in language, domain vocabulary, and abstraction level.3.3.  Intent Resolver   The Intent Resolver is responsible for mapping the semantic   representation of a user's intent to a corresponding policy concept   within the Policy Knowledge Base.  It performs soft matching,   evaluates confidence, and applies a thresholding mechanism to   determine whether to generate a policy or flag the result for   operator intervention.              +-----------------+              | Semantic Mapper |              +-----------------+    +------------------+-----------------------+    |                  |                Intent |    |                  v              Resolver |    |      +----------------------+            |     +-----------+    |      | Soft Matching Engine |<-----------+---->| knowledge |    |      +----------------------+            |     | Base      |    |                  |                       |     +-----------+    |                  v                       |           ^    |       +----------------------+           |           |    |       | Confidence Evaluator |           |           |    |       +----------------------+           |           |    |                  |                       |           |    |                  v                       |           |    |     +--------------------------+         |           |    |     |      Threshold Gate      |         |           |    |     +--------------------------+         |           |    +--------+--------------------+------------+           |         <Forward>            <Degrade> +---------------+  |             v                    +-----|Feedback Logger|--+       +----------+                     +---------------+       | Policy   |       | Composer |       +----------+Gu & Jeong                Expires 23 April 2026                 [Page 9]Internet-Draft         Intent Translation for IoT           October 2025              Figure 3: Intent Resolver Internal Architecture   The internal architecture of the Intent Resolver is shown in   Figure 3.   *  Soft Matching Engine: This component receives the aggregated      intent vector from the Semantic Mapper and computes its semantic      similarity against all relevant policy entries stored in the      Policy Knowledge Base.  The comparison uses a vector-space      similarity metric (e.g., cosine similarity) to identify the best-      matching policy triple.   *  Confidence Evaluator: Once the most relevant policy candidate is      selected, this component evaluates the semantic confidence score      associated with the match.  This score quantifies the degree of      alignment between the user's intent and the closest available      domain policy.   *  Threshold Gate: The confidence score is evaluated against a      configurable semantic threshold.  If the score exceeds the      threshold, the candidate policy is considered a valid match and is      forwarded to the Policy Composer.  If the score falls below the      threshold, the intent is flagged as degraded.   *  Feedback Logger: For degraded matches, this component logs the      failure for later analysis and may optionally initiate human-in-      the-loop review.  If confirmed or corrected, the outcome can be      used to update the Policy Knowledge Base, thus improving future      resolution accuracy.   The Intent Resolver enables explainable, flexible, and confidence-   aware translation of semantic user goals into domain-aligned policy   artifacts.  It ensures that all generated outputs are grounded in   interpretable knowledge while also supporting fallback and learning-   based feedback.   The intent and high-level policy artifacts produced by the Intent   Translator Framework can be expressed in standardized data models   such as XML [RFC6020][RFC7950] or YAML [YAML].  These documents can   be delivered to the appropriate management or orchestration systems   via NETCONF [RFC6241], RESTCONF [RFC8040], or REST API [REST]   interfaces for deployment and enforcement.   As described in the modular architecture of the framework, user-   defined natural language intent is processed through a structured   pipeline that includes the Intent Coordinator, Intent Parser,   Semantic Mapper, Intent Resolver, and Policy Composer.  The semantic   reasoning within this pipeline is grounded in a domain-specificGu & Jeong                Expires 23 April 2026                [Page 10]Internet-Draft         Intent Translation for IoT           October 2025   Policy Knowledge Base, enabling soft matching and degraded intent   handling.  This design ensures that operational goals - even when   imprecisely expressed - can be semantically aligned to existing   policy capabilities, enhancing automation readiness and   interoperability across IoT environments.   Therefore, this document proposes a practical and extensible   architecture for intent translation in next-generation management   systems.  Through this architecture, high-level user goals can be   reliably mapped to structured policy outputs, and network services   can be automatically configured, validated, and adapted.  The   framework enables intent-based management to support scalable,   knowledge-grounded automation in complex service domains such as IoT,   vertical-specific networks, and intelligent edge infrastructures.4.  IANA Considerations   This document does not require any IANA actions.5.  Deployment Considerations   The deployment of the Intent Translator Framework requires alignment   between the domain-specific Policy Knowledge Base and the operational   policies supported by the underlying infrastructure.  Domain-specific   vocabularies, service models, and operational goals must be encoded   within the knowledge base to ensure accurate semantic reasoning.   Operators should pre-train or validate the semantic embedding space   against realistic intents and policy sets before enabling full   automation.  This is particularly important for service domains where   intent ambiguity or synonymy could lead to unintended configurations.6.  Extensibility Considerations   The modular architecture of the framework allows for individual   components - such as the Semantic Mapper or Policy Composer - to be   adapted to different domains, languages, or knowledge graph models.   As such, implementers can substitute the language model used for   prompting, modify the embedding strategy, or replace the output   schema (e.g., YAML, XML) without altering the end-to-end translation   flow.   Additionally, the Policy Knowledge Base may be extended over time   with new policy triples and relations to support evolving service   capabilities.  Such extensions should preserve backward compatibility   by maintaining stable identifiers for core operational concepts.Gu & Jeong                Expires 23 April 2026                [Page 11]Internet-Draft         Intent Translation for IoT           October 20257.  Degradation and Human Oversight Considerations   The framework supports degraded intent resolution via soft matching   and confidence scoring.  While this enables flexible operation in the   presence of vocabulary incompleteness or paraphrasing, operators   should evaluate how degraded matches are handled within their intent   lifecycle.   For high-assurance environments, degraded outputs should be reviewed   by a human operator or routed to a validation pipeline before policy   deployment.  Logging mechanisms should be used to record degraded   cases and their resolution outcomes to improve future model   performance and policy reliability.8.  Security Considerations   The Intent Translation Framework must operate over authenticated and   confidential channels (e.g., TLS/HTTPS) to prevent eavesdropping,   message tampering, or replay attacks by malicious actors.   Implementations should enforce strict certificate validation and   regularly rotate cryptographic keys to maintain transport-layer   security.   Because the system processes free-form natural language intents, it   is vulnerable to adversarially crafted inputs designed to produce   unintended or harmful policies.  Deployments should incorporate input   validation and semantic sanity checks such as confirmation interfaces   for high-impact operations and rate limit or quarantine suspicious   requests for manual review.   Intent collisions where contradictory or overlapping intents are   submitted concurrently can lead to policy conflicts or enforcement   gaps.  The framework must include conflict-detection logic in its   Policy Composer component and either automatically resolve detected   collisions using a documented precedence model or escalate them to   human operators for arbitration.9.  References9.1.  Normative References   [RFC6020]  Bjorklund, M., Ed., "YANG - A Data Modeling Language for              the Network Configuration Protocol (NETCONF)", RFC 6020,              DOI 10.17487/RFC6020, October 2010,              <https://www.rfc-editor.org/info/rfc6020>.Gu & Jeong                Expires 23 April 2026                [Page 12]Internet-Draft         Intent Translation for IoT           October 2025   [RFC6241]  Enns, R., Ed., Bjorklund, M., Ed., Schoenwaelder, J., Ed.,              and A. Bierman, Ed., "Network Configuration Protocol              (NETCONF)", RFC 6241, DOI 10.17487/RFC6241, June 2011,              <https://www.rfc-editor.org/info/rfc6241>.   [RFC7950]  Bjorklund, M., Ed., "The YANG 1.1 Data Modeling Language",              RFC 7950, DOI 10.17487/RFC7950, August 2016,              <https://www.rfc-editor.org/info/rfc7950>.   [RFC8040]  Bierman, A., Bjorklund, M., and K. Watsen, "RESTCONF              Protocol", RFC 8040, DOI 10.17487/RFC8040, January 2017,              <https://www.rfc-editor.org/info/rfc8040>.   [RFC8329]  Lopez, D., Lopez, E., Dunbar, L., Strassner, J., and R.              Kumar, "Framework for Interface to Network Security              Functions", RFC 8329, DOI 10.17487/RFC8329, February 2018,              <https://www.rfc-editor.org/info/rfc8329>.   [RFC9315]  Clemm, A., Ciavaglia, L., Granville, L. Z., and J.              Tantsura, "Intent-Based Networking - Concepts and              Definitions", RFC 9315, DOI 10.17487/RFC9315, October              2022, <https://www.rfc-editor.org/info/rfc9315>.   [RFC9365]  Jeong, J., Ed., "IPv6 Wireless Access in Vehicular              Environments (IPWAVE): Problem Statement and Use Cases",              RFC 9365, DOI 10.17487/RFC9365, March 2023,              <https://www.rfc-editor.org/info/rfc9365>.9.2.  Informative References   [I-D.ietf-i2nsf-applicability]              Jeong, J. P., Hyun, S., Ahn, T., Hares, S., and D. Lopez,              "Applicability of Interfaces to Network Security Functions              to Network-Based Security Services", Work in Progress,              Internet-Draft, draft-ietf-i2nsf-applicability-19, 3 April              2025, <https://datatracker.ietf.org/doc/html/draft-ietf-              i2nsf-applicability-19>.   [I-D.jeong-nmrg-security-management-automation]              Jeong, J. P., Lingga, P., Park, J., Lopez, D. R., and S.              Hares, "An I2NSF Framework for Security Management              Automation in Cloud-Based Security Systems", Work in              Progress, Internet-Draft, draft-jeong-nmrg-security-              management-automation-00, 20 October 2025,              <https://datatracker.ietf.org/doc/html/draft-jeong-nmrg-              security-management-automation-00>.Gu & Jeong                Expires 23 April 2026                [Page 13]Internet-Draft         Intent Translation for IoT           October 2025   [I-D.jeong-nmrg-ibn-network-management-automation]              Jeong, J. P., Ahn, Y., Gu, M., Kim, Y., and J. Jung-Soo,              "Intent-Based Network Management Automation in 5G              Networks", Work in Progress, Internet-Draft, draft-jeong-              nmrg-ibn-network-management-automation-06, 9 June 2025,              <https://datatracker.ietf.org/doc/html/draft-jeong-nmrg-              ibn-network-management-automation-06>.   [SPT]      Lingga, P., Jeong, J., Yang, J., and J. Kim, "SPT:              Security Policy Translator for Network Security Functions              in Cloud-Based Security Services", IEEE Transactions on              Dependable and Secure Computing, Volume 21, Issue 6,              DOI https://doi.org/10.1109/TDSC.2024.3371788, November              2024, <https://doi.org/https://doi.org/10.1109/              TDSC.2024.3371788>.   [YAML]     Ingerson, B., Evans, C., and O. Ben-Kiki, "Yet Another              Markup Language (YAML) 1.0",              Available: https://yaml.org/spec/history/2001-05-26.html,              October 2023.   [TS-23.501]              "System Architecture for the 5G System (5GS)", Available:              https://portal.3gpp.org/desktopmodules/Specifications/              SpecificationDetails.aspx?specificationId=3144, September              2023.   [TS-28.312]              "Intent Driven Management Services for Mobile Networks",              Available:              https://portal.3gpp.org/desktopmodules/Specifications/              SpecificationDetails.aspx?specificationId=3554, September              2023.   [TR-28.812]              "Study on Scenarios for Intent Driven Management Services              for Mobile Networks", Available:              https://portal.3gpp.org/desktopmodules/Specifications/              SpecificationDetails.aspx?specificationId=3553, December              2020.   [TS-23.288]              "Architecture Enhancements for 5G System (5GS) to Support              Network Data Analytics Services", Available:              https://portal.3gpp.org/desktopmodules/Specifications/              SpecificationDetails.aspx?specificationId=3579, September              2023.Gu & Jeong                Expires 23 April 2026                [Page 14]Internet-Draft         Intent Translation for IoT           October 2025   [TS-29.520]              "Network Data Analytics Services", Available:              https://portal.3gpp.org/desktopmodules/Specifications/              SpecificationDetails.aspx?specificationId=3355, September              2023.   [RFC7149]  Boucadair, M. and C. Jacquenet, "Software-Defined              Networking: A Perspective from within a Service Provider              Environment", RFC 7149, March 2014,              <https://www.rfc-editor.org/rfc/rfc7149>.   [Flan-T5]  Chung, H., "Scaling Instruction-Finetuned Language              Models", arXiv arXiv:2210.11416,              Available: https://arxiv.org/abs/2210.11416, October 2022.   [GPT-3]    Brown, T., "Language Models are Few-Shot Learners",              arXiv arXiv:2005.14165,              Available: https://arxiv.org/abs/2005.14165, May 2020.   [USENIX-ATC-Lumi]              Jacobs, A., Pfitscher, R., Ribeiro, R., Ferreira, R.,              Granville, L., Willinger, W., and S. Rao, "Hey, Lumi!              Using Natural Language for Intent-Based Network              Management", USENIX Annual Technical Conference,              Available:              https://www.usenix.org/conference/atc21/presentation/              jacobs, July 2021.   [BERT]     Devlin, J., Chang, M., Lee, K., and K. Toutanova, "BERT:              Pre-training of Deep Bidirectional Transformers for              Language Understanding", NAACL-HLT Conference,              Available: https://aclanthology.org/N19-1423.pdf, June              2019.   [REST]     Author, A., "REST API for Network Management", Available:               https://example.com/rest-spec, July 2025.   [Deep-Learning]              Goodfellow, I., Bengio, Y., and A. Courville, "Deep              Learning", Publisher: The MIT Press,              Available: https://www.deeplearningbook.org/, November              2016.   [Survey-IBN-CST-2023]              Leivadeas, A. and M. Falkner, "A Survey on Intent-Based              Networking",              Available: https://ieeexplore.ieee.org/document/9925251,              March 2023.Gu & Jeong                Expires 23 April 2026                [Page 15]Internet-Draft         Intent Translation for IoT           October 2025Appendix A.  Acknowledgments   This work was supported by Institute of Information & Communications   Technology Planning & Evaluation (IITP) grant funded by the Korea   Ministry of Science and ICT (MSIT) (No.  RS-2024-00398199 and RS-   2022-II221015).   This work was supported in part by Institute of Information &   Communications Technology Planning & Evaluation (IITP) grant funded   by the Korea Ministry of Science and ICT (MSIT) (No. 2025-RS-   2022-II221199, Regional strategic industry convergence security core   talent training business).Appendix B.  Contributors   This document is made by the group effort of OPWAWG, greatly   benefiting from inputs and texts by Linda Dunbar (Futurewei) Yong-   Geun Hong (Daejeon University), and Joo-Sang Youn (Dong-Eui   University).  The authors sincerely appreciate their contributions.   The following are coauthors of this document:   Yoseop Ahn   Department of Computer Science & Engineering   Sungkyunkwan University   2066 Seobu-Ro, Jangan-Gu   Suwon   Gyeonggi-Do   16419   Republic of Korea   Phone: +82 31 299 4106   Email: ahnjs124@skku.edu   URI:   http://iotlab.skku.edu/people-Ahn-Yoseop.phpAuthors' Addresses   Mose Gu (editor)   Department of Computer Science and Engineering   2066 Seobu-Ro, Jangan-Gu   Suwon   Gyeonggi-Do   16419   Republic of Korea   Email: rna0415@skku.eduGu & Jeong                Expires 23 April 2026                [Page 16]Internet-Draft         Intent Translation for IoT           October 2025   Jaehoon Paul Jeong (editor)   Department of Computer Science and Engineering   2066 Seobu-Ro, Jangan-Gu   Suwon   Gyeonggi-Do   16419   Republic of Korea   Phone: +82 31 299 4957   Email: pauljeong@skku.eduGu & Jeong                Expires 23 April 2026                [Page 17]