ISO/IEC JTC 1 SC 42 Artificial Intelligence - Working Group 4
Use Cases & Applications
   07/21/2019
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The quality of use case submissions will be evaluated for inclusion in the Working Group's Technical Report based the application area, relevant AI technologies, credible reference sources (see References section), and the following characteristics:

  • Data Focus & Learning: Use cases for AI system which utilizes Machine Learning, and those that use a fixed a priori knowledge base.
  • Level of Autonomy: Use cases demonstrating several degrees (dependent, autonomous, human/critic in the loop, etc.) of AI system autonomy.
  • Verifiability & Transparency: Use cases demonstrating several types and levels of verifiability and transparency, including approaches for explainable AI, accountability, etc.
  • Impact: Use cases demonstrating the impact of AI systems to society, environment, etc.
  • Architecture: Use cases demonstrating several architectural paradigms for AI systems (e.g., cloud, distributed AI, crowdsourcing, swarm intelligence, etc.)
No. 30 ID: Use Case Name: Autonomous network and automation level definition
Application
Domain
ICT
Deployment
Model
Cyber-physical systems
StatusPoC
Scope
Objective(s)To define autonomous network concept and automation level for the common understanding and consensus
Short
Description
(up to
150 words)
With the goal of providing common understanding and consensus for autonomous self-driving network, this use case delivers a harmonized classification system and supporting definitions that:
  • Define the concept of autonomous network
  • Identify six levels of network automation from “no automation” to “full automation”.
  • Base definitions and levels on functional aspects of technology.
  • Describe categorical distinctions for a step-wise progression through the levels.
  • Educate a wider community by clarifying for each level what role (if any) operators have in performing the dynamic network operations task while a network automation system is engaged.
  • Complete Description The telecom CSPs have a dual challenge – to increase agility while reducing network operating cost.
    1. The exponential growth of network complexity e.g. 5G will make the traditional network O&M model unsustainable;
    2. Digital transformation accelerates service innovation but requires automation capabilities.
    As CSPs start to evaluate their digital transformation strategies, automation is a central concern. Some operators are already introducing automation to some of their network processes, most commonly O&M, planning and optimization. According to Analysys Mason, in 2018, 56% of CSPs globally have little or no automation in their networks. But by 2025, according to their own predictions, almost 80% expect to have automated 40% or more of their network operations, and one-third will have automated over 80%. The introduction of AI/ML (artificial intelligence/machine learning) will be an important part of that process for many CSPs, helping to make the network more intelligent, agile and predictive. The autonomous self-driving network has two essential elements in common with the autonomous self-driving car:
  • There are different levels of automation, relating to different timescales and scenarios
  • Intensive use of artificial intelligence (AI) is essential

    With the goal of providing common understanding and consensus for autonomous self driving network, this use case delivers a harmonized classification system and supporting definitions that set out six levels of automation for the network.

    Level

    Name

    Definition

    Execution

    (Hands)

    Awareness

    (Eyes)

    Decision

    (Minds)

    Experience

    (Hearts)

    System

    Capability

    0

    Manual

    Operation & Maintenance

    Even with auxiliary tools, O&M personnel perform all dynamic tasks.

    P

    P

    P

    P

    n/a

    1

    Assisted

    Operation & Maintenance

    Under the applicable design scope, the system can execute a sub-task repeatedly based on rules.

    P/S

    P

    P

    P

    Sub-task

    level

    2

    Partial

    Autonomous Network

    Under the applicable design scope, the system continuously completes the control task of a unit based on the model.

    S

    P

    P

    P

    Unit level

    3

    Conditional

    Autonomous Network

    Under the applicable design scope, the system can implement complete closed-loop automation of single-domain scenarios. Users can respond to the requests in a timely manner when the system fails.

    S

    S

    P

    P

    Domain level

    4

    Highly

    Autonomous Network

    Under the applicable design scope, the system can automatically analyze and execute cross-domain and service close-loop automation.

    S

    S

    P

    P

    Service level

    5

    Full

    Autonomous Network

    The system can perform complete dynamic tasks and exception handling in all network environments. O&M personnel do not need to intervene.

    S

    S

    S

    P/S

    All Modes


    P=Personnel (Manual), S=System (Automated)
    -Level 0 - manual O&M: The system delivers assisted monitoring capabilities, which means all dynamic tasks have to be executed manually.
    -Level 1 - assisted O&M: The system executes a certain sub-task based on existing rules to increase execution efficiency.
    -Level 2 - partial autonomous network: The system enables closed-loop O&M for certain units under certain external environments, lowering the bar for personnel experience and skills.
    -Level 3 - conditional autonomous network: Building on L2 capabilities, the system can sense real-time environmental changes, and in certain domains, optimize and adjust itself to the external environment to enable intent-based closed-loop management.
    -Level 4 - highly autonomous network: Building on L3 capabilities, the system enables, in a more complicated cross-domain environment, predictive or active closed-loop management of service and customer experience-driven networks. This allows operators to resolve network faults prior to customer complaints, reduce service outages and customer complaints, and ultimately, improve customer satisfaction.
    -Level 5 - full autonomous network: This level is the ultimate goal for telecom network evolution. The system possesses closed-loop automation capabilities across multiple services, multiple domains, and the entire lifecycle, achieving autonomous driving networks.

    The lower levels can be applied now and deliver immediate cost and agility benefits in certain scenarios. An operator can then evolve to the higher levels, gaining additional benefits and addressing a wider range of scenarios.
    Network automation is a long run objective with step-to-step process, from providing an alternative to repetitive execution actions, to performing perception and monitoring of network environment and network device status, making decisions based on multiple factors and policies, and providing effective perception of end user experience. The system capability also starts from some service scenarios and covers all service scenarios.

  • Stakeholders
    Stakeholders'
    Assets,Values
    Systems'
    Threats &
    Vulnerabilities
    Key
    Performance
    Indicators (KPIs)
    Seq. No. Name Description Reference to mentioned
    use case objectives




    AI Features Task(s)All
    Method(s)
    Hardware
    Topology
    Terms &
    Concepts Used
    Autonomous network, self-driving network
    Standardization
    Opportunities
    Requirements
    Challenges
    & Issues
    Societal Concerns Description
    SDGs to
    be achieved
    Industry, Innovation, and Infrastructure
    Data Characteristics
    Description
    Source
    Type
    Volume (size)
    Velocity
    Variety
    Variability
    (rate of change)
    Quality
    Scenario Conditions
    No. Scenario
    Name
    Scenario
    Description
    Triggering Event Pre-condition Post-Condition






    Scenario Name Training
    Step No. Event Name of
    Process/Activity
    Primary
    Actor
    Description of
    Process/Activity
    Requirement






    Specification of training data
    Scenario Name Evaluation
    Step No. Event Name of
    Process/Activity
    Primary
    Actor
    Description of
    Process/Activity
    Requirement






    Input of Evaluation
    Output of Evaluation
    Scenario Name Execution
    Step No. Event Name of
    Process/Activity
    Primary
    Actor
    Description of
    Process/Activity
    Requirement






    Input of Execution
    Output of Execution
    Scenario Name Retraining
    Step No. Event Name of
    Process/Activity
    Primary
    Actor
    Description of
    Process/Activity
    Requirement






    Specification of retraining data
    References
    No. Type Reference Status Impact of
    use case
    Originator
    Organization
    Link








  • Peer-reviewed scientific/technical publications on AI applications (e.g. [1]).
  • Patent documents describing AI solutions (e.g. [2], [3]).
  • Technical reports or presentations by renowned AI experts (e.g. [4])
  • High quality company whitepapers and presentations
  • Publicly accessible sources with sufficient detail

    This list is not exhaustive. Other credible sources may be acceptable as well.

    Examples of credible sources:

    [1] B. Du Boulay. "Artificial Intelligence as an Effective Classroom Assistant". IEEE Intelligent Systems, V 31, p.76-81. 2016.

    [2] S. Hong. "Artificial intelligence audio apparatus and operation method thereof". N US 9,948,764, Available at: https://patents.google.com/patent/US20150120618A1/en. 2018.

    [3] M.R. Sumner, B.J. Newendorp and R.M. Orr. "Structured dictation using intelligent automated assistants". N US 9,865,280, 2018.

    [4] J. Hendler, S. Ellis, K. McGuire, N. Negedley, A. Weinstock, M. Klawonn and D. Burns. "WATSON@RPI, Technical Project Review".
    URL: https://www.slideshare.net/jahendler/watson-summer-review82013final. 2013