Soft computing for content generation: Trading market in a basketball management video game

Abstract

Although procedural and assisted content generation have attracted a lot of attention in both academic and industrial research in video games, there are few cases in the literature in which they have been applied to sport management games. The on-line variants of these games produce a lot of information concerning how the users interact with each other in the game. This contribution presents the application of soft computing techniques in the context of content generation for an on-line massive basketball management simulation game (in particular in the virtual trading market of the game). This application is developed in two different directions: (1) a machine learning model to analyze the appeal of the trading market contents (the virtual basketball players in the game), and (2) an evolutionary algorithm to assist users in the design of new contents (training of virtual basketball players).