An Integrated Framework for AI Assisted Level Design in 2D Platformers

These tools don't provide feedback neither about the functional characteristic of a level (e.g. its difficulty and feasibility), nor about the type of player experience a level will enable. Accordingly, designers either perform extensive playtest or build ad-hoc tools to support their content creation activities [1]. In the recent years, artificial intelligence tools have been proposed to assist game designers in the creation of game content like for example the generation of levels that affect players in terms of emotions [2], [3] or that satisfy structural constraints on the position of game elements [4], [5], [6]. In this context, level generation is usually based on design metrics extracted from the level structure (e.g., the position of game elements) or gameplay features (e.g., using data describing players' skill and playing style obtained with sessions of playtesting). In this paper, we present a framework for the design of levels for 2D platformers that extends previous approaches [2], [3] by providing immediate feedback about the functional properties of levels such as (i) the difficulty and probability of success of single jumps (the main mechanic of platformer games), and (ii) a set of statistics to evaluate the difficulty and probability of completion of entire levels. Our framework has been developed as a modular extension of the popular Unity game engine and it is smoothly integrated in its editor.