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.
Human-inspired strategies to solve complex joint tasks in multi agent systems / Auletta, F.; di Bernardo, M.; Richardson, M. J.. - 54:17(2021), pp. 105-110. (Intervento presentato al convegno 6th IFAC Conference on Analysis and Control of Chaotic Systems CHAOS 2021 tenutosi a ita nel 2021) [10.1016/j.ifacol.2021.11.033].
Human-inspired strategies to solve complex joint tasks in multi agent systems
di Bernardo M.;
2021
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
