A Dataset Schema for Cooperative Learning from Demonstration in Multi-robots Systems

TL;DR

This paper introduces a new dataset schema to facilitate learning coordinated behaviors in multi-agent systems from demonstrations, validated through a multi-robot system experiment.

Contribution

It proposes a novel dataset schema for cooperative learning in MASs from demonstrations, addressing coordination issues and improving agent decision alignment.

Findings

Validated in a multi-robot system setting

Generated new cooperative plan recommendations

Enhanced agent coordination through demonstration learning

Abstract

Multi-Agent Systems (MASs) have been used to solve complex problems that demand intelligent agents working together to reach the desired goals. These Agents should effectively synchronize their individual behaviors so that they can act as a team in a coordinated manner to achieve the common goal of the whole system. One of the main issues in MASs is the agents' coordination, being common domain experts observing MASs execution disapprove agents' decisions. Even if the MAS was designed using the best methods and tools for agents' coordination, this difference of decisions between experts and MAS is confirmed. Therefore, this paper proposes a new dataset schema to support learning the coordinated behavior in MASs from demonstration. The results of the proposed solution are validated in a Multi-Robot System (MRS) organizing a collection of new cooperative plans recommendations from the demonstration by domain experts.