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.)
Use Case Name:
Autonomous network and automation level definition
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.
The telecom CSPs have a dual challenge – to increase agility while reducing network operating cost.
The exponential growth of network complexity e.g. 5G will make the traditional network O&M model unsustainable;
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.
with auxiliary tools, O&M personnel perform all dynamic tasks.
the applicable design scope, the system can execute a sub-task repeatedly
based on rules.
the applicable design scope, the system continuously completes the control
task of a unit based on the model.
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.
the applicable design scope, the system can automatically analyze and execute
cross-domain and service close-loop automation.
system can perform complete dynamic tasks and exception handling in all
network environments. O&M personnel do not need to intervene.
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.
Peer-reviewed scientific/technical publications on AI applications (e.g. ).
Patent documents describing AI solutions (e.g. , ).
Technical reports or presentations by renowned AI experts (e.g. )
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:
 B. Du Boulay. "Artificial Intelligence as an Effective Classroom Assistant". IEEE Intelligent Systems, V 31, p.76-81. 2016.
 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.
 M.R. Sumner, B.J. Newendorp and R.M. Orr. "Structured dictation using intelligent automated assistants". N US 9,865,280, 2018.
 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