Banca de DEFESA: MARCO ANTONIO COSTA SIMOES

Uma banca de DEFESA de DOUTORADO foi cadastrada pelo programa.
STUDENT : MARCO ANTONIO COSTA SIMOES
DATE: 05/07/2022
TIME: 09:00
LOCAL: Remoto
TITLE:

Learning by Demonstration of Coordinated Plans in Multi-Agent Systems

KEY WORDS:

Demonstration learning, Robotics, Multi-agent systems

PAGES: 100
BIG AREA: Ciências Exatas e da Terra
AREA: Ciência da Computação
SUBÁREA: Sistemas de Computação
SPECIALTY: Arquitetura de Sistemas de Computação
SUMMARY:

One of the great challenges in MAS is the creation of cooperative plans to deal with the different scenarios that present themselves in a dynamic, real-time environment composed of teams of mobile robots. In this scenario, an agent of the MAS controls each robot, which needs to make complex decisions in a short time in a coordinated manner with the other robots on its team. Despite the many solutions developed based on multi-agent planning and reinforcement learning, a human expert in the problem domain usually sees opportunities for better cooperative plans in many scenarios where robots underperform. The research presented in this thesis consists of capturing the human expert's knowledge to demonstrate how robot teams can better cooperate in solving the problem they must solve. The human expert can indicate the situations in which a cooperative plan can better solve a given problem by watching the performance of a team of robots in action.
Consequently, a dataset for training the agents that control the robots can gather the various human observations. For the development of this research, this work used the environment 3DSSIM and the collection of human demonstrations was carried out through a set of tools developed from the adaptation of existing solutions in the RoboCup community using a strategy of crowdsourcing. In addition, fuzzy clustering was used to gather expert demonstrations (setplays) with the same semantic meaning, even with small differences. With the data organized, this thesis used a reinforcement learning mechanism to learn a classification policy that allows agents to decide which group of setplays is best suited to each situation that presents itself in the environment. The results show the ability of the robot team to evolve, from the learning of the suggested setplays and its use in an appropriate way to the abilities of each robot.

BANKING MEMBERS:
Presidente - 2115505 - TATIANE NOGUEIRA RIOS
Interna - 1708274 - RITA SUZANA PITANGUEIRA MACIEL
Externo à Instituição - REINALDO AUGUSTO DA COSTA BIANCHI
Externo à Instituição - JOÃO ALBERTO FABRO
Externo à Instituição - LUIS PAULO GONÇALVES DOS REIS - UNIPORTO