In the evolving field of mixed-initiative game design, where procedural content generation plays a pivotal role, establishing a comprehensive approach that empowers non-technical designers to actively shape content generation is essential. Recent developments in large language models significantly alter the landscape of automated text-based content generation. These models offer a significant advantage in mixed-initiative procedural level generation by providing designers with intuitive, natural language interfaces. The framework presented in this paper interprets natural language inputs, detailing level design constraints and optimization goals, to aid in the cooperative development of game levels for a strategy game aimed at environmental sustainability education. It enables designers to articulate their vision concerning the problem domain, goal metrics, and desired difficulty level through a textual description. By utilizing large language models, the framework extracts semantic constraints and optimization objectives, which are then used to generate candidate game levels. The efficacy of these levels is assessed by game-playing agents trained through advanced deep reinforcement learning methods, ensuring alignment with the designer's original specifications. We further evaluate our framework with both experts and non-experts in designing levels for our strategy game. Their detailed responses confirm that our framework effectively translates natural language descriptions into playable game levels, accurately capturing the designers' intended objectives.

As the role of procedural content generation in mixed-initiative game design continues to grow, it is crucial to develop an end-to-end approach that enables non-technical designers to artfully guide content generation. Recent advances in large language models, such as GPT-4, are rapidly transforming the landscape of automated generation of text-based content. Large language models have significant potential for mixed-initiative procedural level generation by providing natural language interfaces for designers. This paper presents an end-to-end procedural level generation framework that interprets natural language descriptions of level design constraints and optimization objectives to facilitate the collaborative creation of game levels for a strategy game focused on environmental sustainability education. The framework enables designers to specify a problem domain, goal metrics, and target difficulty via natural language description. It then employs large language models for the semantic extraction of constraints and optimization targets to drive the generation of candidate levels. Generated game levels are evaluated via game-playing agents trained with deep reinforcement learning techniques to ensure the game levels meet the level designer’s specifications. Manual evaluation by authors shows that the proposed framework can effectively transform designers’ natural language descriptions into fully playable game levels that reflect their intended design objectives.