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Change-Id: Ic5f9950a5b97746108c852ea69e508feacfd843c
Signed-off-by: liuwh7 <liuwh7@asiainfo.com>
Issue-ID: USECASEUI-525

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+.. contents::
+   :depth: 3
+..
+.. _docs_intent_based_network:
+
+
+Intent Based Network
+=============================
+
+Overall Blueprint
+-----------------
+Intent-based network (IBN) is a self-driving network that uses decoupling 
+network control logic and closed-loop orchestration techniques to automate 
+application intents. An IBN is an intelligent network, which can automatically 
+convert, verify, deploy, configure, and optimize itself to achieve target 
+network state according to the intent of the operators, and can automatically 
+solve abnormal events to ensure the network reliability. 
+
+REQ-453 Smart Operator Intent Translation in UUI based on IBN - R8 5G Slicing Support
+In R8, the smart operator intent translation function is proposed to support 
+the 5G slicing selection of current E2E usecase in UUI. 
+The target architecture of the Intent-Based Network is divided into a Intent 
+orchestration layer (hereinafter referred to as the Intent layer), a control 
+layer and a network layer.
+
+
+Abbreviations
+-------------
+
++---------------+--------------------------------------------+
+|  Abbreviation |                   Meaning                  |
++===============+============================================+
+| IBN           | Intent Based Network                       |
++---------------+--------------------------------------------+
+
+
+
+Scope of Honolulu release
+-----------------------
+The scope for Honolulu developed in UUI includes GUI, UUI-server, and NLP.
+
+GUI
+- Services
+- 5G Slicing Management
+- Package Management
+- NLP Model resource
+
+Server
+- Intent Management Module
+
+NLP Server
+(new Micro-service)
+Three NLP algorithms are considered to be applied in current solutions: 
+- BERT (Bidirectional Encoder Representations from Transformers)：developed by researchers at Google AI Language. It has caused a stir in the Machine Learning community by presenting state-of-the-art results in a wide variety of NLP tasks. BERT’s key technical innovation is applying the bidirectional training of Transformer, a popular attention model, to language modelling.
+
+
+
+Impacted Modules for Honolulu
+---------------------------
+
+U-UI
+~~~~
+Target of R8: translate from the human inputs to the slice parameters based on NLP 
+in UUI, and then run the slices based on the current ONAP.
+
+A new page is required in the UUI that users can enter network requirements through 
+the natural language, which then sends the user input to the IBN component and displays 
+the response information to the user.  This process can be repeated several times 
+until the dialog completes and a new Intent is formed in the IBN component.
+
+
+
+Functional Test Cases
+---------------------
+
+
+
+Operation Guidance
+------------------