Abstract
Abstract In this paper we propose a methodology to integrate human expertise with effective control laws to drive artificial agents in a complex joint task. We use Supervised Machine Learning to derive human-inspired strategies that succeed in task performance independently from the operating conditions of the samples provided in the training phase. Numerical simulations validate the efficiency of the proposed human-inspired strategies against simpler yet computationally expensive rule-based strategies.
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