ML Model Initialization via Intent Clustering
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Solution Overview
Problem
Existing methods for implementing machine learning (ML) in complex systems, such as telecommunications networks, face inefficiencies in training ML models to satisfy dynamic intents, as they require maintaining multiple optimized models or training from an untrained state, which is impractical and resource-intensive.
Innovation Solution
The method involves mapping an intent to an intent cluster, setting initialisation parameters based on the cluster, and training an ML model using intent-specific state transition information, leveraging multi-task meta learning for efficient model training and resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple optimized ML models are maintained for different intents, then the system can satisfy diverse criteria effectively, but the device complexity and resource requirements increase significantly
Solution Approach 1:
The patent implements a universal ML model that can handle multiple different intents and criteria through dynamic configuration rather than maintaining separate specialized models. The single model is designed to be multi-functional, adapting its behavior based on the specific intent cluster assigned to it, thereby reducing the number of models needed while maintaining versatility across diverse optimization criteria.
Solution Approach 2:
The system changes parameters dynamically by assigning different intent clusters to the ML model based on the specific criteria that need to be satisfied. Instead of changing the model structure, the system adjusts the intent cluster parameter and corresponding configuration settings to adapt to different optimization goals, enabling one model to serve multiple purposes.
2Adaptability or versatility
If ML models are trained from an untrained state for each new intent, then the model can be customized to specific criteria, but the training time and processing resources increase significantly
Solution Approach 1:
The system performs preliminary action by pre-defining multiple intent clusters with their associated configurations and criteria before actual operation. When a new intent arrives, the system can quickly map it to an existing cluster or combine clusters rather than starting from scratch, significantly reducing training time while maintaining customization capability through the intent cluster framework.
Solution Approach 2:
The system uses copying by replicating successful intent cluster configurations and applying them to new similar intents. Instead of training entirely new models for each intent, the system copies and adapts proven configurations from existing intent clusters, reducing training requirements while maintaining effectiveness for the new intent.
3Device complexity
If a single ML model is used for all intents, then the device complexity is reduced, but the model cannot effectively satisfy diverse and dynamic criteria
Solution Approach 1:
The system introduces dynamics by making the intent cluster assignment flexible and adaptable rather than static. The single ML model can dynamically switch between different intent clusters based on the current optimization goals and criteria, allowing it to effectively handle diverse requirements while maintaining a simple single-model architecture. The intent cluster configuration can be updated and reassigned as needed.
Data Source
AI summary
Methods and apparatus for addressing intents using machine learning (ML) are provided. A method of operation for a node implementing ML, wherein the node instructs actions in an environment in accordance with a policy generated by a ML agent, and wherein the ML agent models the environment, includes obtaining an intent, wherein the intent specifies one or more criteria to be satisfied by the environment. The method further includes determining an intent cluster from among a plurality of intent clusters to which the intent maps, the determination being based on the criteria specified by the intent, and setting initialisation parameters for a ML model to be used to model the intent, based on the determined intent cluster. The method also includes training the ML model using training data specific to the intent, and generating one or more suggested actions to be performed on the environment using the trained ML model.