A Q-Learning-Based Approach for Simple and Multi-Agent Systems

This study proposes different machine learning-based solutions to both single and multi-agent systems, took place on a 2-D simulation platform, namely, Robocode. This dynamic and programmable platform allows agents to interact with the environment and each other by employing a variety of battling strategies. Q-Learning is one of the leading and popular machine learning-based solutions to be applied to such a problem. However, especially for continued spaces, the control problem gets deeper. Essentially, one of the main drawbacks of reinforcement learning (RL) is to design an appropriate reward function that the function can be described by only employing few parameters for simple tasks, whereas estimating the goal of the reward function may be a challenging problem. Recent studies prove that neural network-based approaches can handle these challenges and achieve to learn control strategies from 2-D or 1-D data. Besides those problems of RL algorithms for single robots, once the number of robots increases and the systems need to behave as multi-agent systems, the overall design requirements become more complex. Accordingly, the proposed system is validated by considering different battle scenarios. The performance of the Q-Learning-based system and the supervised learning techniques are compared by employing different scenarios for this problem. Results reveal the superiority of the ANN-based approach over other methods

Proposta de Aprendizagem Integrada de Matemática e Programação com Abordagens do Pensamento Computacional no Jogo Robocode

Estudantes das novas gerações, imersos num contexto digital, podem demonstrar desmotivações quando inseridos somente em ambientes considerados como tradicionais para ensino de disciplinas como matemática. Planejar ambientes com jogos digitais podem ser uma estratégia lúdica para ensino de matemática e programação de computadores. A presente proposta apresenta desafios no ambiente do jogo educacional com simulador de robôs, Robocode. Os desafios foram construídos a partir das abordagens do Pensamento Computacional e apresentam aportes entre conceitos matemáticos e de programação de computadores para determinar estratégias de comportamento e a programação dos robôs dentro do jogo. Os conteúdos pertinentes à matemática estão relacionados ao ensino e aprendizagem do plano cartesiano e são integrados à linguagem de programação com conceitos introdutórios na orientação à objetos. Os resultados apontam que a integração das disciplinas em um ambiente de jogo, devidamente planejado, pode apoiar o ensino e aprendizagem dos conteúdos matemáticos e computacionais de modo lúdico

Multi Agent Systems

Research on multi-agent systems is enlarging our future technical capabilities as humans and as an intelligent society. During recent years many effective applications have been implemented and are part of our daily life. These applications have agent-based models and methods as an important ingredient. Markets, finance world, robotics, medical technology, social negotiation, video games, big-data science, etc. are some of the branches where the knowledge gained through multi-agent simulations is necessary and where new software engineering tools are continuously created and tested in order to reach an effective technology transfer to impact our lives. This book brings together researchers working in several fields that cover the techniques, the challenges and the applications of multi-agent systems in a wide variety of aspects related to learning algorithms for different devices such as vehicles, robots and drones, computational optimization to reach a more efficient energy distribution in power grids and the use of social networks and decision strategies applied to the smart learning and education environments in emergent countries. We hope that this book can be useful and become a guide or reference to an audience interested in the developments and applications of multi-agent systems

Learning programming language with problem based learning methodology in scientific competitions with Robocode

Orientador: Marcos Augusto Francisco BorgesDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de TecnologiaResumo: Alunos das gerações atuais, inseridos em um contexto de conectividade no universo digital, podem considerar desmotivadores ambientes de ensino tradicionais para disciplinas de programação. Este trabalho apresenta uma abordagem para ensino-aprendizagem de linguagem de programação, a partir do desenvolvimento de competições científicas baseadas no jogo digital educacional Robocode. O objetivo está associado em avaliar se competições científicas com Robocode estimulam aprendizagem de conceitos de programação de computadores em cursos técnicos e superiores da área de informática. O presente trabalho baseia-se nos conceitos da metodologia de aprendizado baseado em problemas (PBL ¿ Problem Based Learning) associado ao Robocode. Técnicas de pesquisa, como estudo de caso e questionários, foram utilizados com objetivo em identificar as potencialidades que o ambiente de ensino-aprendizagem pode proporcionar nas disputas entre equipes envolvendo conceitos de linguagem de programação Java. O trabalho discute a evolução, entre as competições Robocode do LIAG. Com a participação de professores apoiados no PBL, foram determinadas estruturas para os problemas gerados no jogo durante as competições. Professores com papel junto às equipes competidoras, de forma a prover suporte no sequenciamento e levantamento de hipóteses para resolução dos desafios inerentes à competição Robocode. O resultado do desenvolvimento de ambientes com métodos diversificados de ensino-aprendizagem, apontam as competições como uma abordagem efetiva quando o objetivo é auxiliar o estímulo de alunos com métodos lúdicos e colaborativos na aprendizagem de conceitos de linguagem de programação. A experiência de organização de competições científicas com envolvimento interinstitucional com base no jogo educacional Robocode foi analisada ao longo de dois anos, na Liga 2015 e Robocode Brasil 2016Abstract: Students of current generations, inserted in a context of connectivity in the digital universe may consider demotivating the traditional teaching environment for programing disciplines. This study presents an approach to teaching-learning programing language from the development of scientific competitions based on the educational digital game Robocode. The goal is associated with evaluating if scientific competitions with Robocode stimulate the learning of computers programing concepts in technical and superior courses of Computer Science área. The present work is based on problem-based learning (PBL) methodology concepts associated to Robocode. Research techniques, such as case studies and questionnaires were used to identify the potentialities the teaching-learning environment can provide in disputes among teams involving Java programing language concepts. The paper discuss the evolution, between the LIAG competitions Robocode. With teachers participation of supported in the PBL, structures were determined for problems generated in the game during the competitions. Teachers act together with the competing teams, in order to provide support in the sequencing and hypotheses to solve the challenges inherent to the Robocode competition. The result of the development of environment of diversified teaching-learning methods points out the competition an effective approach when the goal is to assist the stimulation of students with playful and collaborative methods in learning concepts of language programing. The experience of organizing scientific competition with interinstitutional involvement based on Robocode educational game was analyzed over two years, in the 2015 League and Brazil Robocode 2016MestradoTecnologia e InovaçãoMestre em Tecnologi