In this demo paper we present Prompt Override, a serious game in which players engage in prompt-based hacking challenges by manipulating the system prompt of a large language model (LLM) to solve puzzles. The game features two LLMs: one as the target of the player's hacking attempts, and another as a rogue assistant guiding the player throughout the game. As players explore a simulated file system, editing prompt snippets, Prompt Override offers a unique take on the hacking game genre-one where rigid solution paths are replaced with more open-ended, language-driven problem solving. By leveraging realworld limitations and vulnerabilities of LLMs, Prompt Override encourages players to develop a deeper understanding of prompt engineering, model behavior, and the ethical implications of interacting with intelligent systems.

This paper introduces the Procedural Content Generation Benchmark for evaluating generative algorithms on different game content creation tasks.The benchmark comes with 12 game-related problems with multiple variants on each problem.Problems vary from creating levels of different kinds to creating rule sets for simple arcade games.Each problem has its own content representation, control parameters, and evaluation metrics for quality, diversity, and controllability.This benchmark is intended as a first step towards a standardized way of comparing generative algorithms.We use the benchmark to score three baseline algorithms: a random generator, an evolution strategy, and a genetic algorithm.Results show that some problems are easier to solve than others, as well as the impact the chosen objective has on quality, diversity, and controllability of the generated artifacts.

Autonomously tailoring content to a set of pre-determined affective patterns has long been considered the holy grail of affect-aware human-computer interaction at large. The experience-driven procedural content generation framework realises this vision by searching for content that elicits a certain experience pattern to a user. In this paper, we propose a novel reinforcement learning (RL) framework for generating affect-tailored content, and we test it in the domain of racing games. Specifically, the experience-driven RL (EDRL) framework is given a target arousal trace, and it then generates a racetrack that elicits the desired affective responses for a particular type of player. EDRL leverages a reward function that assesses the affective pattern of any generated racetrack from a corpus of arousal traces. Our findings suggest that EDRL can accurately generate affect-driven racing game levels according to a designer's style and outperforms search-based methods for personalised content generation. The method is not only directly applicable to game content generation tasks but also employable broadly to any domain that uses content for affective adaptation.

Tools for designing content require a medium that allows the designer to efficiently express their creativity, and a system that ensures the content being designed adheres to the domain of interest. Interacting with Large Language Models (LLMs) via natural language is extremely intuitive for a human designer, although it remains largely unexplored. However, this approach has a limitation: LLMs are prone to hallucinations and they tend to ignore parts of the user request in their responses. One workaround is to let LLM use tools such as function calling to ensure consistency of the content. We formalise this approach by proposing LLMaker, a general framework for consistent video game content generation empowered by LLMs, bridging the gap between creative vision and technical execution. We demonstrate LLMaker’s application in generating dungeon crawler level layouts, comparing it against alternative LLM-based methods for content generation over multiple tests, testing for consistency of the outputs and elapsed time per request.

In this demo paper we present LLMaker, a videogame level design tool that operates solely via natural language instructions. Unlike existing level design tools, LLMaker allows the designer to express their intent in an intuitive way, interacting with a cognitively undemanding interface, while still ensuring the generated levels adhere to specific domain and playability constraints.

In mixed-initiative co-creation tasks, wherein a human and a machine jointly create items, it is important to provide multiple relevant suggestions to the designer. Quality-diversity algorithms are commonly used for this purpose, as they can provide diverse suggestions that represent salient areas of the solution space, showcasing designs with high fitness and wide variety. Because generated suggestions drive the search process, it is important that they provide inspiration, but also stay aligned with the designer's intentions. Additionally, often many interactions with the system are required before the designer is content with a solution. In this work, we tackle these challenges with an interactive constrained MAP-Elites system that leverages emitters to learn the preferences of the designer and then use them in automated steps. By learning preferences, the generated designs remain aligned with the designer's intent, and by applying automatic steps, we generate more solutions per user interaction, giving a larger number of choices to the designer and thereby speeding up the search. We propose a general framework for preference-learning emitters (PLEs) and apply it to a procedural content generation task in the video game Space Engineers. We built an interactive application for our algorithm and performed a user study with players.

When generating content for video games using procedural content generation (PCG), the goal is to create functional assets of high quality. Prior work has commonly leveraged the feasible-infeasible two-population (FI-2Pop) constrained optimisation algorithm for PCG, sometimes in combination with the multi-dimensional archive of phenotypic-elites (MAP-Elites) algorithm for finding a set of diverse solutions. However, the fitness function for the infeasible population only takes into account the number of constraints violated. In this paper we present a variant of FI-2Pop in which a surrogate model is trained to predict the fitness of feasible children from infeasible parents, weighted by the probability of producing feasible children. This drives selection towards higher-fitness, feasible solutions. We demonstrate our method on the task of generating spaceships for Space Engineers, showing improvements over both standard FI-2Pop, and the more recent multi-emitter constrained MAP-Elites algorithm.