Rapid modeling of human-defined AI behavior patterns in games

Abstract

The creation of computer and videogames is a challenging and multidisciplinary endeavor, requiring different approaches to integrate different disciplines while keeping relatively low development costs. In this context, domain specific languages (DSLs) are increasingly becoming a valid tool, allowing nonprogrammers to participate in the development process. In this work, we focus on a DSL developed within the Gade4all project, focused on defining the behaviors of non-player characters, proposing a design which allows the participation of users with a lack of programming knowledge to define behavior and interaction complex patterns of in-game enemies in a simple and straightforward way. This preferable to other approaches to defining opponent AIs in games, such as machine learning techniques (which produce advanced AI opponents that do not behave as humans) or rigid rule systems created by programmers rather than behavioral experts. The approach has been tested by comparing the creation of well-known AI patterns using T2GAME with popular game editors, and resulted in significantly reduced development times, in addition to being more approachable for non-programmers such as behavioral psychologists.