Autonomous Learning with Automatically Created Models and a Novel Model Selection
Harshal V Bharatia · 2021 IEEE Symposium Series on Computational Intelligence (SSCI) · 2021
An autonomous learning approach is presented here for expansive problem domains that may undergo frequent changes. It is hard to train and adapt learning-models to changes when the problem domain is very large. With the autonomous learning approach, a system performs knowledge refinement automatically by determining what can be improved and trains itself without explicit guidance to do so. It uses novel techniques to automatically create the learning-models and select an optimal model for each prediction. It automatically builds dynamic ensembles of models that incorporate specific improvisations, such as improvements based on past knowledge or specific trends reflected in the results, and overshadows sub-optimal portions of existing model. A reinforcement learning based model selector identifies which model is optimal for handling a request using a novel approach with automatically created hierarchical states. As a result, smart adaptive solutions that go beyond initial training become possible. This also alleviates the need for big complex monolithic models that require extensive training, automatically adapts to changes in the domain and offers better control over its performance. Experimental results show this approach learns quickly, adapts to changes very rapidly and performs quite well against prevalent learning methods.