A Dataset Schema for Cooperative Learning from Demonstration in Multi-robots Systems
This addresses coordination issues in multi-robot systems for domain experts, but it is incremental as it builds on existing learning from demonstration methods.
The paper tackles the problem of agents' coordination in Multi-Agent Systems (MASs) by proposing a new dataset schema to support learning coordinated behavior from demonstration, validated in a Multi-Robot System with recommendations of cooperative plans.
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.