Evolving Intelligent Multimodal Gameplay Agents and Decision Makers with Neuroevolution

�Super Mario Bros� is a difficult platforming game that requires the use of multiple behavioral modes to complete different gameplay elements such as: collecting coins, dodging enemies and getting to the end of the level. Methods for creating intelligent game playing agents have previously used human designed behavior policy for each gameplay state or by combining gameplay goals into a single task to be learned. This thesis assesses the development and method of training machines to promote multiple modes of behavior within neural network controllers. These controllers utilize the concept of evolution through multi-objective optimization for the test bench platform game system �MarioAI�. Artificial neural networks were evolved to exhibit complex and multimodal behavior using multiple sub objectives of the game; and thus overcome the non-linear, noisy, and fractured game environment. Experiments were conducted with the purpose of creating multiple Pareto-optimal solutions of quality with differing behavioral aspects. These solutions were then discerned by a Decision Maker Neural Network Ensemble that had been evolved to pick the best solution according to game level. This Decision Maker Ensemble proved to be able to learn on minimal information and provide the highest overall game score. The results of this thesis show that it�s possible to train agents on sub objectives to teach multiple forms of complex behavior that can then be abstractly chosen by an evolved Decision Maker to provide a better outcome than agents that were trained specifically towards that single solution.Electrical Engineerin

Mobile Robotics

The book is a collection of ten scholarly articles and reports of experiences and perceptions concerning pedagogical practices with mobile robotics.“This work is funded by CIEd – Research Centre on Education, project UID/CED/01661/2019, Institute of Education, University of Minho, through national funds of FCT/MCTES-PT.

Mimicking human player strategies in fighting games using game artificial intelligence techniques

Fighting videogames (also known as fighting games) are ever growing in popularity and accessibility. The isolated console experiences of 20th century gaming has been replaced by online gaming services that allow gamers to play from almost anywhere in the world with one another. This gives rise to competitive gaming on a global scale enabling them to experience fresh play styles and challenges by playing someone new. Fighting games can typically be played either as a single player experience, or against another human player, whether it is via a network or a traditional multiplayer experience. However, there are two issues with these approaches. First, the single player offering in many fighting games is regarded as being simplistic in design, making the moves by the computer predictable. Secondly, while playing against other human players can be more varied and challenging, this may not always be achievable due to the logistics involved in setting up such a bout. Game Artificial Intelligence could provide a solution to both of these issues, allowing a human player s strategy to be learned and then mimicked by the AI fighter. In this thesis, game AI techniques have been researched to provide a means of mimicking human player strategies in strategic fighting games with multiple parameters. Various techniques and their current usages are surveyed, informing the design of two separate solutions to this problem. The first solution relies solely on leveraging k nearest neighbour classification to identify which move should be executed based on the in-game parameters, resulting in decisions being made at the operational level and being fed from the bottom-up to the strategic level. The second solution utilises a number of existing Artificial Intelligence techniques, including data driven finite state machines, hierarchical clustering and k nearest neighbour classification, in an architecture that makes decisions at the strategic level and feeds them from the top-down to the operational level, resulting in the execution of moves. This design is underpinned by a novel algorithm to aid the mimicking process, which is used to identify patterns and strategies within data collated during bouts between two human players. Both solutions are evaluated quantitatively and qualitatively. A conclusion summarising the findings, as well as future work, is provided. The conclusions highlight the fact that both solutions are proficient in mimicking human strategies, but each has its own strengths depending on the type of strategy played out by the human. More structured, methodical strategies are better mimicked by the data driven finite state machine hybrid architecture, whereas the k nearest neighbour approach is better suited to tactical approaches, or even random button bashing that does not always conform to a pre-defined strategy

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