Algoritmos genéticos aplicados à análise estratégica de jogos de tabuleiro

Abstract

Genetic Algorithms (GAs) are a technique to obtain approximate solutions that consist of evolution concepts such as: heredity, mutation, natural selection and recombination. Genetic Algorithms have been used in board games such as “Chess” and “Checkers” to find better strategies, including the analysis of the adversary’s behavior. Board games are, besides hobbies, a form of logical and mathematical development. One of the most known board games is Monopoly, played all over the world by people of different ages and profiles. This is mainly a turn-based strategy game, although the state of a player in the short term is highly influenced by dice rolling. This work intends to analyze intelligent agents (using GAs) to identify the best game strategy. A Monopoly simulator with simplified rules was developed to set the actions of the intelligent agents. The implementation demanded some restrictions given that certain game mechanisms demand a higher processing, because the game starts having more states, enhancing its analysis complexity