System and method for adative training data delivery using domain-specific performance monitoring reference to related applications

A system for adaptive training data delivery optimization, the system comprising:

• a communication management server comprising one or more processors, a memory, and a plurality of programming instructions stored in the memory, the plurality of programming instructions when executed by the one or more processors causes the one or more processors to:
• receive entity interaction data from a plurality of entities;
• generate N (N−1)/2 training data therefrom, wherein the generated training data comprises multi-dimensional relationship vectors, relationship labels, and domain identifiers;
• determine whether defined state models with transition probabilities are available;
• responsive to the availability of the defined state models with transition probabilities, utilize model-based delivery implementing model-based reinforcement learning to deliver training data to AI systems;
• responsive to non-availability of the defined state models with transition probabilities, utilize model-free delivery implementing model-free reinforcement learning using neural networks to deliver training data to the AI systems;
• receive, performance effectiveness metrics from the AI systems receiving the training data, wherein performance effective metrics comprise prediction accuracy, learning convergence rate, generalization capability, and computational efficiency;
• identify a domain associated with the training data;
• determine, for the domain, designated primary metrics among the performance effectiveness metrics;
• for each primary metric, compare the primary metric against respective domain-specific threshold;
• responsive to any of the primary metric being below respective domain-specific threshold, switch the delivery mechanism between the model-based delivery and the model-free delivery and vice versa, wherein an adaptive switching mechanism switches a current delivery mode between the model-free delivery and the model-based delivery and vice versa;
• responsive to all of the primary parameters being above respective domain-specific thresholds, calculate a composite metric for secondary metrics, wherein the secondary metrics are the performance effectiveness metrics left after the primary parameters;
• and
• responsive to the composite metric being below the universal threshold, optimize the current delivery approach.