Uma abordagem para maximização da produção de recursos em jogos RTS

• Ano de defesa: 2012
• Autor(a) principal: Naves, Thiago França
• 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: Busca estocástica; Jogos; Planejamento; Recursos; Tempo real; Jogos por computador; Games; Planning; Real-time; Resources; Stochastic search; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: https://repositorio.ufu.br/handle/123456789/12534; https://doi.org/10.14393/ufu.di.2012.317

Real-time strategy Games (RTS) are an important field of research in artificial intelligence planning. In these games, we have to deal with characteristics that present challenges for the planning, such as time constraints, numerical effects and actions with many preconditions. RTS games are characterized by two important phases. The first, where is necessary gather resources and build an army via resource production. In the second, the resources produced in the first phase are used in battles involving defense and attack against the enemy. Thus, the first phase becomes vitally important for the success of the player within the game, and maximize the production of resources to elevate the maximum power of the army developed, reflects directly on the chances of victory. This research is focused on the first phase of the game. To maximize the amount of resources produced in the game is necessary to determine goals that produce resources such scale, considering quality goals. Large part of the works in planning for games not consider the search for goals that have quality or criteria that benefit planning for use within the game. Thus, to stipulate goals we propose an approach that maximizes the resource production using Simulated Annealing (SA) along with planning techniques. The approach uses stochastic search to maximize production of resources generating plans of action that produce such resources within the game and can be considered as goals with quality planning to be achieved. For the correct operation of this work, the SA was adapted to operate on the domain of real time, and have also been developed planners and consistency checkers to assist it in this task. As a result, the approach was efficient in its use within the environment of an RTS game, where the resource production was able to match with human players during testing. Thus, this research aims to fill a gap present in related works of planning for RTS games, in relation to the production of resources based on criteria.