Human-inspired strategies to solve complex joint tasks in multi agent systems

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.