Action Selection for Game Play Agents Using Genetic Algorithms in Platform Game Computational Intelligence Competitions

Abstract

The application of computational intelligence (CI) and artificial intelligence (AI) to games has been attempted as a typical implementation of intelligent processing on computers. Intelligence in this sense is understood as the ability to search for the best solution efficiently among multiple options, specifically in AI playing board games such as chess. As the processing ability of computers increases, CI/AI systems are outperforming humans in finding potential solutions from a tremendous number of options within a short timeframe. These days, computer games are widely prevalent. CI/AI applications in computer games are focused on animating non-player characters (NPCs), whereas CI/AI applications in the scientific fields are focused on modeling intelligent human activities. The field of computer games faces many issues, such as dealing with dynamic environments that change quickly and processing images at higher resolutions and complexity. The use of computer games as a benchmark for CI/AI technologies has been attempted, and competitions involving various kinds of games have been held to encourage innovation in the field. In this paper, we describe a learning agent that participated in a platform game CI competition held in conjunction with Fuzzy System Symposium (FSS 2012). The approach adopted in this paper is a basic method based on conventional methods. The authors hope that this presentation of our development processes would encourage many researchers to participate in competitions and that it would contribute to progress in the field.