Cinematography techniques in games aim to provide high-level abstractions for operating a virtual camera on the basis of concepts borrowed from the movie industry, such as scene, shot, and line of action, among others. One typical approach is the development of agents to execute tasks that are similar to their counterparts in a real movie set: director, editor, and cinematographer. However, a game is an interactive application and resembles a live TV show or a live sport transmission, where the actions taken by all the actors are not known previously. In such a scenario, the role of the editor is of great importance, since he or she is the one who ultimately decides what point of view should appear on the screen. In the context of game cinematography, most previous work has proposed ways of mapping scene concepts and automatically controlling the virtual camera, but without paying much attention to the role of an editor agent. Our article demonstrates an intelligent editor agent that uses neuronal network classifiers to decide shot transition and has an intuitive user interface to the learning mechanism.