Video game archaeology is a relatively new field. This can involve studying players through the traces they leave in digital game worlds, though only limited work of this kind exists. Furthermore, the potential of these methods to record ephemeral play experiences for preservation purposes has not been widely explored. We conducted an archaeological survey of five sites in Elden Ring, taking place directly before, during and after the release of a major expansion. We present what is, to our knowledge, the first collaborative autoethnography of an archaeological survey in a video game, reflecting on our recorded footage, notes and data. Through a diffractive analysis, we demonstrate the value of video game archaeology as a form of hauntological practice that allows for a deeper reflection on the knowledge production process, and in doing so contribute to the development of new interdisciplinary methodologies in HCI, archaeology and games research.

Generative Artificial Intelligence (GenAI) applications in artistic and creative domains have gained substantial attention of late. These intelligent interactive systems, shaped by innovations in Large Language Models (LLMs) and Vision Language Models (VLMs), are materially impacting digital creative domains. While initial work to understand this space has highlighted new models and architectures, we lack a holistic view of how interactive GenAI systems are designed for user interactions across various artistic and creative domains. In this paper, we present a systematic review of interactive GenAI system designs for art and creativity in the HCI literature (N = 189), and a detailed taxonomy of interaction paradigms with design components. We shed light on the communities of design focus and decompose the system interaction designs, mapping these characteristics to creative domains, user interaction patterns, GenAI technologies, detailing under-represented spaces, and future directions of designing interactions for GenAI creativity.

Exploration is found in a variety of game genres, but there has been little research in the context of PCG. This paper investigates the potential for exploratory agents to provide feedback on how well levels support exploration, with the ultimate goal of guiding level generation. We propose several motivations which might drive exploratory behaviour and model these as metrics within an agent framework based on context steering. We present a study of how the different metrics influence exploration of six game levels. It was found that combinations of metrics lead to distinct exploratory behaviours, mostly within our expectations.

Massively multiplayer online (MMO) games boomed in the late 1990s to 2000s. In parallel, ethnographic studies of these communities emerged, generally involving participant observation and interviews. Several decades on, many MMOs have been reconfigured, remastered or are potentially no longer accessible at all, which presents challenges for their continued study and long-term preservation. In this paper we explore the “go-along” methodology, in which a researcher joins a participant on a walk through a familiar place and asks them questions, as a qualitative research method applicable for the study and preservation of games culture. Though the methodology has been introduced in digital media studies, to date it has had limited application in digital games, if at all. We report on a pilot study exploring applications of the go-along method to the sandbox MMO Wurm Online; a persistent, player-directed world with a rich history. We report on our motivations for the work, our analysis of the resulting interviews, and our reflections on both the use of go-alongs in digital games, as well as the unique and inspiring culture and community of this lesser-known game.

A game’s theme is an important part of its design – it conveys narrative information, rhetorical messages, helps the player intuit strategies, aids in tutorialisation and more. Thematic elements of games are notoriously difficult for AI systems to understand and manipulate, however, and often rely on large amounts of hand-written interpretations and knowledge. In this paper we present a technique which connects game embeddings, a recent method for modelling game dynamics from log data, and word embeddings, which models semantic information about language. We explain two different approaches for using game embeddings in this way, and show evidence that game embeddings enhance the linguistic translations of game concepts from one theme to another, opening up exciting new possibilities for reasoning about the thematic elements of games in the future.

The evaluation of procedural content generation (PCG) systems for generating video game levels is a complex and contested topic. Ideally, the field would have access to robust, generalisable and widely accepted evaluation approaches that can be used to compare novel PCG systems to prior work, but consensus on how to evaluate novel systems is currently limited. We argue that the field can benefit from a structured analysis of how procedural level generation systems can be evaluated, and how these techniques are currently used by researchers. This analysis can then be used to both inform on the current state of affairs, and to provide data to justify changes to this practice. This work aims to provide this by first developing a novel taxonomy of PCG evaluation approaches, and then presenting the results of a survey of recent work in the field through the lens of this taxonomy. The results of this survey highlight several important weaknesses in current practice which we argue could be substantially mitigated by 1) promoting use of evaluation free system descriptions where appropriate, 2) promoting the development of diverse research frameworks, 3) promoting reuse of code and methodology wherever possible.

Procedural content generation has been applied to many domains, especially level design, but the narrative affordances of generated game environments are comparatively understudied. In this paper we present our first attempt to study these effects through the lens of what we call a generative archaeology game that prompts the player to archaeologically interpret the generated content of the game world. We report on a survey that gathered qualitative and quantitative data on the experiences of 187 participants playing the game Nothing Beside Remains. We provide some preliminary analysis of our intentional attempt to prompt player interpretation, and the unintentional effects of a glitch on the player experience of the game.

Although procedural generation is popular among game developers, academic research on the topic has primarily focused on new applications, with some research into empirical analysis. In this paper we relate theoretical work in information theory to the generation of content for games. We prove that there is a relationship between the Kolomogorov complexity of the most complex artifact a generator can produce, and the size of that generator’s possibility space. In doing so, we identify the limiting relationship between the knowledge encoded in a generator, the density of its output space, and the intricacy of the artifacts it produces. We relate our result to the experience of expert procedural generator designers, and illustrate it with some examples.

In this paper we survey the motivations behind contemporary game AI research by analysing individual publications, the researchers themselves, and the institutions that influence them. In doing so, we identify some negative effects on our field, caused both by external forces outside of our control as well as institutionalised behaviours that are easily overlooked. We suggest how we might begin to address some of these issues as a community, and reassert ourselves as the primary driving force behind the field.

Archaeology can be broadly defined as the study and interpretation of the past through material remains. Video game worlds, though immaterial in nature, can also afford opportunities to study the people who existed within them based on what they leave behind. In this paper we present one of the first formal archaeological surveys of a predominantly single-player game, by recording the player-generated content that is asynchronously distributed to players in Elden Ring. We report on the methodology and results of the survey, before reflecting on what we could extrapolate from those results about Elden Ring’s player community, and the nature of archaeological surveying within video games more broadly.

The GDMC AI settlement generation challenge is a procedural content generation (PCG) competition about producing an algorithm that can create a settlement in the game Minecraft. In contrast to the majority of AI competitions, the GDMC entries are evaluated by human experts on several criteria such as adaptability, functionality, evocative narrative, and visual aesthetics – all of which represent challenges to state-of-the-art PCG systems. This paper contains a collection of written experiences with this competition, by participants, judges, organizers and advisors. We asked people to reflect both on the artifacts themselves, and on the competition in general. The aim of this paper is to offer a shareable and edited collection of experiences and qualitative feedback which have the potential to push forward PCG and computational creativity, but would be lost once the individual assessments are compressed to scalar ratings. We reflect upon organizational issues for AI competitions, and discuss the future of the GDMC competition.

In order to advance the field of procedural content generation, and transfer knowledge from academic research to everyday use, we need to develop tools that make generative systems easier to understand and control. In this article, we introduce Danesh, a plugin to the unity game development environment, which helps provide a suite of tools that provide automation or analysis of different aspects of procedural generators. We describe here the features of Danesh, including automatic analysis of generated content, the visualization of generative spaces, automatic parameter discovery, and interface smoothing. We also provide reflections on our development of the tool so far.

Automatically analyzing games is an important challenge for automated game design, general game playing, and cocreative game design tools. However, understanding the nature of an unseen game is extremely difficult due to the lack ofa prioridesign knowledge and heuristics. In this article, we formally definehyperstate space graphs, a compressed form of state-space graphs, which can be constructed without any prior design knowledge about a game. We show how hyperstate space graphs produce compact representations of games, which closely relate to the heuristics designed by hand for search-based artificial intelligence (AI) agents; we show how hyperstate space graphs relate to modern ideas about game design; we report on exploratory uses of hyperstate space graphs as an analytical tool, and we point toward future applications for hyperstates across game AI research.

Over the last decade we have watched as artificial intelligence has been transformed into one of the most important issues of our time, and games have grown into the biggest entertainment industry. As a result, game AI research as a field has enjoyed increased access to funding, exposure in the press, and influence with governments and some of the largest technology firms in the world. At this pivotal moment in the history of our field, this paper argues that this privileged position brings with it an important set of responsibilities which we have largely failed to meet. We show to whom we are responsible, identify some of these responsibilities, and suggest actions we can take as a community to leverage this power for good.

Monte Carlo Tree Search (MCTS) has been shown to be an effective algorithm for solving search problems in the absence of heuristics. MCTS performs best in spaces where every action has a significant impact on the outcome of the search. In many domains, however, particularly single-player games, many actions have little impact on the outcome, which makes MCTS perform poorly without heuristic support. To address this deficiency in such sparse-impact search problems, we introduce MCTS with Reversibility Compression, or MCTS-R, which uses the notion of action reversibility to compress MCTS trees as they are constructed, without loss of information. This not only reduces the memory footprint of the search tree, but also accelerates search by preventing the duplication of already-explored states, and increasing the attention paid to significant actions. We show that our approach outperforms several comparable algorithms for solving sparse-impact search problems.

Procedural generation is used to achieve a wide variety of game design goals, and has led to the creation of several game subgenres by injecting variance, surprise or unpredictability into otherwise static designs. Information games are a type of mystery game in which the player is tasked with gathering knowledge and developing an understanding of an event or system. Their reliance on player knowledge leaves them vulnerable to spoilers and hard to replay. In this paper we introduce the notion of a generative forensics game, a subgenre of information games that challenge the player to understand the output of a generative system, and present two examples.

As we develop more assistive and automated game design systems, the question of how these systems should be integrated into game development workflows, and how much adaptation may be required, becomes increasingly important. In this paper we explore the impact of software engineering decisions on the ability of an automated game design system to understand a game's codebase, generate new game code, and evaluate its work. We argue that a new approach to software engineering may be required in order for game developers to fully benefit from automated game designers.

Tabletop roleplaying games (TTRPGs) and procedural content generators can both be understood as systems of rules for producing content. In this paper, we argue that TTRPG design can usefully be viewed as procedural content generator design. We present several case studies linking key concepts from PCG research – including possibility spaces, expressive range analysis, and generative pipelines – to key concepts in TTRPG design. We then discuss the implications of these relationships and suggest directions for future work uniting research in TTRPGs and PCG.

Most generative systems built in game development are parameter-driven, but the relationship between parameters and the output of the system is often unclear. This makes them frustrating to use for both experts and novices, and as a result generators are often filtered post-hoc, or tweaked through time-consuming trial and error. In this paper we introduce two analytical techniques: smoothness and codependence. We show how these features help analyse the impact of a parameter change on a generative system and suggest ways this could feed back into more intelligent tools that make working with procedural generators more precise and pleasant.

Automatically analysing games is an important challenge for automated game design, general game playing, and co-creative game design tools. However, understanding the nature of an unseen game is extremely difficult due to the lack of a priori design knowledge and heuristics. In this paper we formally define hyperstate space graphs, a compressed form of state space graphs which can be constructed without any prior design knowledge about a game. We show how hyperstate space graphs produce compact representations of games which closely relate to the heuristics designed by hand for search-based AI agents; we show how hyperstate space graphs also relate to modern ideas about game design; and we point towards future applications for hyperstates across game AI research.

The papers in this special section focus on game design based on artificial intelligence (AI). As researchers working with games, we are often faced with terms that either lack a definition entirely or which are contested and debated as part of their very nature. Many papers have tried and failed to define what “fun” is, from where “creativity” originates, or what “difficulty” means, for example, and many more papers in the future will try and fail to do the same. Tangling with ineffable concepts, with moving targets and nebulous ideas is all part of the joy of doing artificial intelligence (AI) research in a complex, multifaceted domain like games.

Tarot has been used for centuries as a method to give structure to storytelling, both in game and fortune-telling settings. As such, tarot cards have developed over time, expanding the symbolism and depth of meaning associated with each card. This provides a corpus for a large number of possible stories, making tarot a rich area of exploration for story generation. Therefore, we have created a tarot-based narrative generation system that creates short movie-like story synopses, along with a tagline one might see on a movie poster. This project is in early development; we have created a prototype as a proof of concept. The project exists as a webpage that an interactor can use to draw new tarot cards for the story spread (card layout) and generate new stories from them. In this paper we discuss the details of our system and describe more details about the tarot as a corpus. We also discuss future areas of exploration based on our proof of concept.

Automatically generating content for videogames has long been a staple of game development and the focus of much successful research. Such forays into content generation usually concern themselves with producing a specific game component, such as a level design. This has proven a rich and challenging area of research, but in focusing on creating separate parts of a larger game, we miss out on the most challenging and interesting aspects of game development. By expanding our scope to the automated design of entire games, we can investigate the relationship between the different creative tasks undertaken in game development, tackle the higher level creative challenges of game design, and ultimately build systems capable of much greater novelty, surprise, and quality in their output. This paper, the first in a series of two, describes two case studies in automating game design, proposing cooperative coevolution as a useful technique to use within systems that automate this process. We show how this technique allows essentially separate content generators to produce content that complements each other. We also describe systems that have used this to design games with subtle emergent effects. After introducing the technique and its technical basis in this paper, in the second paper in the series we discuss higher level issues in automated game design, such as potential overlap with computational creativity and the issue of evaluation.

Procedural content generation is generally viewed as a means to an end-a tool employed by designers to overcome technical problems or achieve a particular design goal. When we move from generating single parts of games to automating the entirety of their design, however, we find ourselves facing a far wider and more interesting set of problems than mere generation. When the designer of a game is a piece of software, we face questions about what it means to be a designer, about computational creativity, and about how to assess the growth of these automated game designers and the value of their output. Answering these questions can lead to new ideas in how to generate content procedurally, and produce systems that can further the cutting edge of game design. This paper describes work done to take an automated game designer and advance it towards being a member of a creative community. We outline extensions made to the system to give it more autonomy and creative independence, in order to strengthen claims that the software is acting creatively. We describe and reflect upon the software's participation in the games community, including entering two game development contests, and show the opportunities and difficulties of such engagement. We consider methods for evaluating automated game designers as creative entities, and underline the need for automated game design to be a major frontier in future games research.