No abstract

Figure 1: The six different classes where a player can fail while playing videogames.

Most tutorials in video games do not consider the skill level of the player when deciding what information to present. This makes many tutorials either tedious for experienced players or not informative enough for players who are new to the given genre. With Talin, implemented as an asset in the Unity game engine, we make it possible to create a mastery model of an individual player's skill levels by operationalizing Dan Cook's skill atom theory. We propose that using this mastery model opens up a new design space when it comes to designing tutorials. We show an example tutorial implementation with Talin assembled using only graphical components provided by our framework, without the need of writing any code. The dynamic tutorial implementation results in the player receiving information only when they need it, whenever they need it. While the novice player is given all the information they need to learn the system, the expert player is not bogged down by tooltip pop-ups regarding mechanics they have already mastered.

Advances in artificial intelligence (AI) techniques have resulted in immense breakthroughs in how well we can algorithmically play videogames. Yet, this increased investment in game-playing AI (GPAI) techniques has not translated into a tangible improvement in the game-playing experience for our players. This paper is inspired by the positive impact of accessibility modes in recent games as well as previous calls in games research literature to focus on player experience. Responding to these calls, we propose utilizing GPAI techniques not to beat the player, as is traditionally done, but to support them in fully experiencing the game. We claim that utilizing GPAI agents to help players overcome barriers is a productive way of repurposing the capabilities of these agents. We further contribute a design exercise to help developers explore the space of possible GPAI-driven assistance methods. This exercise helps developers discover types of challenges and ideate methods that vary in magnitude and assistance type. We first apply this design exercise to explore the design space of possible assistance methods for the action platformer game Celeste. We then implement two of the discovered methods that target different challenge types in a Unity clone of Celeste. Through this implementation, we discover several additional research questions we must answer before GPAI-driven assistance methods can be truly effective. We believe this research direction furthers the discussion on how to utilize GPAI in service of the player experience and also contributes to the creation of more inclusive games.

Academic procedural content generation (PCG) efforts often yield plausible game content but stop short of fully integrating it into the games that inspired it. This misses opportunities to discover game- and platform-specific constraints that were previously ignored in evaluations of playability (e.g. how invisible objects in a level design are used to explicitly control camera movement). Grappling with existing games can ensure that the PCG community is solving realistic problems, rather than convenient abstractions of them. In this paper, we use technical knowledge from the ROM hacking community along with the WaveFunctionCollapse example-driven generator to reinject controllably-generated level designs into the commercial Super Metroid ROM image (rather than a clone) for execution on physical Nintendo hardware. Our work charts a path for more realistic evaluation of the playability of generated content and highlights challenges for deploying generative methods. These challenges can spark a conversation about the ways that abstractions are used in PCG research.