MP-Draughts - Um Sistema Multiagente de Aprendizagem Automática para Damas Baseado em Redes Neurais de Kohonen e Perceptron Multicamadas

• Ano de defesa: 2009
• Autor(a) principal: Duarte, Valquíria Aparecida Rosa
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Uberlândia; BR; Programa de Pós-graduação em Ciência da Computação; Ciências Exatas e da Terra; UFU
• Palavras-chave em Português: Sistemas multi-agentes; Algoritmos de clusterização; Redes neurais artificiais; Aprendizagem por reforço; Aprendizagem por diferenças temporais; Busca eficiente; Jogos; Inteligência artificial; Jogos (Dama); Redes neurais - Computação; Multiagent system; Clustering algorithm; Artificial neural network; Reinforcement learning; Temporal difference learning; Network; Efficient search; Game; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: https://repositorio.ufu.br/handle/123456789/12478

The goal of this work is to present MP-Draughts (MultiPhase- Draughts), that is a multiagent environment for Draughts, where one agent - named IIGA- is built and trained such as to be specialized for the initial and the intermediate phases of the games and the remaining ones for the final phases of them. Each agent of MP-Draughts is a neural network which learns almost without human supervision (distinctly from the world champion agent Chinook). MP-Draughts issues from a continuous activity of research whose previous product was the efficient agent VisionDraughts. Despite its good general performance, VisionDraughts frequently does not succeed in final phases of a game, even being in advantageous situation compared to its opponent (for instance, getting into endgame loops). In order to try to reduce this misbehavior of the agent during endgames, MP-Draughts counts on 25 agents specialized for endgame phases, each one trained such as to be able to deal with a determined cluster of endgame boardstates. These 25 clusters are mined by a Kohonen-SOM Network from a Data Base containing a large quantity of endgame boardstates. After trained, MP-Draughts operates in the following way: first, an optimized version of VisionDraughts is used as IIGA; next, the endgame agent that represents the cluster which better fits the current endgame board-state will replace it up to the end of the game. This work shows that such a strategy significantly improves the general performance of the player agents.