A Method for Generating Emergent Behaviors Using Machine Learning to Strategy Games

This work proposes the use of machine learning for the creation of a basic library of experiences, which will be used for the generation of emergent behaviors for characters in a strategy game. In order to create a high diversification of the agents' story elements, the characteristics of the agents are manipulated based on their adaptation to the environment and interaction with enemies. We start by defining important requirements that should be observed when modeling the instances. Then, we propose a new architecture paradigm and suggest what would be the most appropriate classification algorithm for this architecture. Results are obtained with an implementation of a prototype strategy game, called Darwin Kombat, which validated the definition of the best classifier.