Modding is a creative practice through which players negotiate the structure and values of games to better reflect the priorities of their communities. This paper analyzes the popularity and categories of active mods for Stardew Valley, Skyrim, and Cyberpunk 2077 on the website Nexus Mods. Through an initial thematic analysis, game-specific categories were mapped into broader forms of change, including character identities, environments, and gameplay expansions. We additionally use keyword and text-based analysis of mod summaries, descriptions, and tags to identify more specific patterns, particularly around value-oriented themes such as accessibility and identity. Across all three games, several broad priorities recur, including strong support for user interface changes, gameplay and story expansions, and the modding tools that make further modification possible. At the same time, each game exhibits a distinct pattern of creative priorities: Skyrim is dominated by atmosphere and VFX-based interface, animation, and gameplay expansion mods, Cyberpunk 2077 by character identity and appearance mods, and Stardew Valley by NPCs, environments, and quality-of-life upgrades. Additionally, targeted analysis of accessibility and identity-related mods suggest that these themes are strongly valued and supported by player communities. These early results suggest that players use mods to express their creativity and change who games are for and how they are experienced. Through these findings, this paper aims to position modding as a form of creative negotiation through which player communities transform games to better reflect their own values.

We introduce the Amorphous Fortress-an abstract, open-ended artificial life simulation framework. In this system, entities are represented as finite-state machines (FSMs) which allow for multi-agent interaction within a constrained space. These agents are created by randomly generating and evolving the FSMs; sampling from pre-defined states and transitions. This environment was designed to explore the emergent AI behaviors found implicitly in simulation games such as Dwarf Fortress or The Sims. We apply two evolutionary algorithms to this environment, hill-climber and MAP-Elites, to explore the various levels of depth and interaction from the generated FSMs and to generate diverse sets of environments that exhibit dynamics estimated to be complex by analyses of agents' FSM architecture and activation. This paper combines the work of two previous non-archival workshop papers.

This paper studies how large language models (LLMs) can act as effective, high-level creative collaborators and “muses” for game design. We model the design of this study after the exercises artists use by looking at amorphous ink splotches for creative inspiration. Our goal is to determine whether AI-assistance can improve, hinder, or provide an alternative quality to games when compared to the creative intents implemented by human designers. The capabilities of LLMs as game designers are stress tested by placing it at the forefront of the decision making process. Three prototype games are designed across 3 different genres: (1) a minimalist base game, (2) a game with features and game feel elements added by a human game designer, and (3) a game with features and feel elements directly implemented from prompted outputs of the LLM, ChatGPT. A user study was conducted and participants were asked to blindly evaluate the quality and their preference of these games. We discuss both the development process of communicating creative intent to an AI chatbot and the synthesized open feedback of the participants. We use this data to determine both the benefits and shortcomings of AI in a more design-centric role.

This article describes a new version of the mixed-initiative collaborative level-designing system, i.e.,Baba Is Y'all, as well as the results of a user study on the system.Baba is Y'allis a prototype for artificial intelligence (AI)-assisted game design in collaboration with others. The updated version includes a more user-friendly interface, a better level evolver and recommendation system, and extended site features. The system was evaluated via a user study where participants were required to play a previously submitted level from the site, and then create their own levels using the editor. They reported on their individual process creating the level and their overall experience interacting with the site. The results have shown both the benefits and limitations of this mixed-initiative system and how it can help with creating a diversity ofBaba is Youlevels that are both human and AI designed while maintaining their quality.

The open-world sandbox game Minecraft is well-known for applying a wide array of procedural content generation techniques to create unique and expansive game environments. However, procedurally generated buildings are absent in the Minecraft world, thus players must build their own structures to flesh out their worlds. This build process can be extremely time-consuming and appeals to more creatively-inclined players. To aid players in this process, we introduce a tool combining interactive evolution with latent variable evolution to evolve procedurally generated Minecraft structures to a player’s aesthetic choices. We employ two separate neural network models to generate structures: a 3D generative model for generating the structure design and an encoding model for applying Minecraft textures to the structure’s voxels. We evaluate this tool with a user study incorporating an online interface that allows participants to select, evolve, and guide a population of these generated 3D structures towards a specific design goal.

We investigate how to efficiently predict play personas based on playtraces. Play personas can be computed by calculating the action agreement ratio between a player and a generative model of playing behavior, a so-called procedural persona. But this is computationally expensive and assumes that appropriate procedural personas are readily available. We present two methods for estimating play personas, one using regular supervised learning and aggregate measures of game mechanics initiated, and another based on sequence learning on a trace of closely cropped gameplay observations. While both of these methods achieve high accuracy when predicting play personas defined by agreement with procedural personas, they utterly fail to predict play style as defined by the players themselves using a questionnaire. This interesting result highlights the value of using computational methods in defining play personas.

The Keke AI Competition introduces an artificial agent competition for the game Baba is You - a Sokoban-like puzzle game where players can create rules that influence the mechanics of the game. Altering a rule can cause temporary or permanent effects for the rest of the level that could be part of the solution space. The nature of these dynamic rules and the deterministic aspect of the game creates a challenge for AI to adapt to a variety of mechanic combinations in order to solve a level. This paper describes the framework and evaluation metrics used to rank submitted agents and baseline results from sample tree search agents.

In this paper, we present a method for automated persona-driven video game tutorial level generation. Tutorial levels are scenarios in which the player can explore and discover different rules and game mechanics. Procedural personas can guide generators to create content which encourages or discourages certain playstyle behaviors. In this system, we use procedural personas to calculate the behavioral characteristics of levels which are evolved using the quality-diversity algorithm known as Constrained MAP-Elites. An evolved map’s quality is determined by its simplicity: the simpler it is, the better it is. Within this work, we show that the generated maps can strongly encourage or discourage different persona-like behaviors and range from simple solutions to complex puzzle-levels, making them perfect candidates for a tutorial generative system.

We present a collaborative mixed-initiative system for building levels for the puzzle game "Baba is You". Unlike previous mixed-initiative systems, Baba is Y'all is designed for collaborative asynchronous creation by multiple users over the internet. The system includes several AI-assisted features to help designers, including a level evolver and an automated player for playtesting. The level archives catalogues levels according to which mechanics are implemented and not implemented, allowing the system to ask users to design levels with specific combinations of mechanics. We describe the operation of the system and the results of small-scale informal user test, and discuss future development paths for this system as well as for collaborative mixed-initiative systems in general.

This paper introduces a fully automatic method of mechanic illumination for general video game level generation. Using the Constrained MAP-Elites algorithm and the GVG-AI framework, this system generates the simplest tile based levels that contain specific sets of game mechanics and also satisfy playability constraints. We apply this method to illuminate the mechanic space for four different games in GVG-AI: Zelda, Solarfox, Plants, and RealPortals. With this system, we can generate playable levels that contain different combinations of most of the possible mechanics. These levels can later be used to populate game tutorials that teach players how to use the mechanics of the game.