Graph-based Generation of Action-Adventure Dungeon Levels using Answer Set Programming

The construction of dungeons in typical action-adventure computer games entails composing a complex arrangement of structural and temporal dependencies. It is not simple to generate dungeons with correct lock-and-key structures. In this paper we sketch a controllable approach to building graph-based models of acyclic dungeon levels via declarative constraint solving, that is capable of satisfying a range of hard gameplay and design constraints. We use a quantitative expressive range analysis to characterise the initial output of the system, present an example of the degree to which the output may be altered, and show a comparison with an alternate approach

Curious Users of Casual Creators

Casual creators are a type of design tool identified by Compton & Mateas, characterised by an orientation towards enjoyable, intrinsically motivated creative exploration, rather than task-oriented designer productivity. In our experiments holding rapid game jams with Wevva, a casual creator for mobile game design, we have noticed, however, that users seem to vary considerably even within the context of using a casual creator. Some people focus on designing specific games, while others explore the design space extensively, or even focus exclusively on prodding the edges of the design space looking for its possibilities and limits. We hypothesise that the latter group of users is driven primarily by curiosity about a casual creator and its design space. This results in different patterns of behaviour to the former group (of design-oriented users), which may worth characterising and perhaps explicitly designing for

Generation and Analysis of Content for Physics-Based Video Games

The development of artificial intelligence (AI) techniques that can assist with the creation and analysis of digital content is a broad and challenging task for researchers. This topic has been most prevalent in the field of game AI research, where games are used as a testbed for solving more complex real-world problems. One of the major issues with prior AI-assisted content creation methods for games has been a lack of direct comparability to real-world environments, particularly those with realistic physical properties to consider. Creating content for such environments typically requires physics-based reasoning, which imposes many additional complications and restrictions that must be considered. Addressing and developing methods that can deal with these physical constraints, even if they are only within simulated game environments, is an important and challenging task for AI techniques that intend to be used in real-world situations. The research presented in this thesis describes several approaches to creating and analysing levels for the physics-based puzzle game Angry Birds, which features a realistic 2D environment. This research was multidisciplinary in nature and covers a wide variety of different AI fields, leading to this thesis being presented as a compilation of published work. The central part of this thesis consists of procedurally generating levels for physics-based games similar to those in Angry Birds. This predominantly involves creating and placing stable structures made up of many smaller blocks, as well as other level elements. Multiple approaches are presented, including both fully autonomous and human-AI collaborative methodologies. In addition, several analyses of Angry Birds levels were carried out using current state-of-the-art agents. A hyper-agent was developed that uses machine learning to estimate the performance of each agent in a portfolio for an unknown level, allowing it to select the one most likely to succeed. Agent performance on levels that contain deceptive or creative properties was also investigated, allowing determination of the current strengths and weaknesses of different AI techniques. The observed variability in performance across levels for different AI techniques led to the development of an adaptive level generation system, allowing for the dynamic creation of increasingly challenging levels over time based on agent performance analysis. An additional study also investigated the theoretical complexity of Angry Birds levels from a computational perspective. While this research is predominately applied to video games with physics-based simulated environments, the challenges and problems solved by the proposed methods also have significant real-world potential and applications

The Dungeon Variations Problem Using Constraint Programming

The video games industry generates billions of dollars in sales every year. Video games can offer increasingly complex gaming experiences, with gigantic (but consistent) open worlds, thanks to larger and larger teams of developers and artists. In this paper, we propose a constraint-based approach for procedural dungeon generation in an open world/universe context, in order to provide players with consistent, open worlds with an excellent quality of storytelling. Thanks to a global description capturing all the possible rooms and situations of a given dungeon, our approach allows enumerating variations of this global pattern, which can then be presented to the player for more diversity. We formalise this problem in constraint programming by exploiting a graph abstraction of the dungeon pattern structure. Every path of the graph represents a possible variation matching a given set of constraints. We introduce a new propagator extending the "connected" graph constraint, which allows considering directed graphs with cycles. We show that thanks to this model and the proposed new propagator, it is possible to handle scenarios at the forefront of the game industry (AAA+ games). We demonstrate that our approach outperforms non-specialised solutions consisting of filtering only the relevant solutions a posteriori. We then conclude and offer several exciting perspectives raised by this approach to the Dungeon Variations Problem

Languages of games and play: A systematic mapping study

Digital games are a powerful means for creating enticing, beautiful, educational, and often highly addictive interactive experiences that impact the lives of billions of players worldwide. We explore what informs the design and construction of good games to learn how to speed-up game development. In particular, we study to what extent languages, notations, patterns, and tools, can offer experts theoretical foundations, systematic techniques, and practical solutions they need to raise their productivity and improve the quality of games and play. Despite the growing number of publications on this topic there is currently no overview describing the state-of-the-art that relates research areas, goals, and applications. As a result, efforts and successes are often one-off, lessons learned go overlooked, language reuse remains minimal, and opportunities for collaboration and synergy are lost. We present a systematic map that identifies relevant publications and gives an overview of research areas and publication venues. In addition, we categorize research perspectives along common objectives, techniques, and approaches, illustrated by summaries of selected languages. Finally, we distill challenges and opportunities for future research and development

Learning to Identify Bugs in Video Games

The use of intelligent software agents promises to revolutionise video game testing. While agents automate the time-consuming task of repeatedly playing a game in search of issues, humans can spend their time on the more creative aspects of game development. Despite the substantial advancements in game-playing that have made this possible, agents are reliant on humans, or hand-crafted guards, to determine whether there are issues with the game's design or functioning. This thesis aimed to develop testing agents that can identify issues with a game's function or bugs with minimal human involvement by learning from their prior experiences. The problem is framed as one of anomaly detection, where bugs correspond to abnormality or novelty in an agent's experience. A series of approaches based on Self-Supervised Learning and Causal Inference have been developed to enable an agent to measure abnormality or otherwise model the game to subsequently identify bugs. The focus was on laying the foundations for testing agents that operate over the same input/output modalities as human testers. The approaches were evaluated by testing a diverse collection of purpose-built video games, where they successfully identified bugs from a broad class. This thesis is among the first work to investigate the use of machine learning in the context of video game bug identification. It presents an exposition of the problem of learning intended behaviour, and then endeavours to develop solutions that demonstrate the benefits of using agents with learning capabilities for testing. Namely, ease of reuse across projects (reusability) and in identifying bugs that would otherwise require human involvement to be found (capability). The use of agents equipped with sophisticated game-playing algorithms and the identification tools outlined in this thesis offers a new framework for video game testing

Player Expectations of Strategy Game AI

The behaviour of AI in modern strategy games is universally recognised as flawed. To compensate for this and successfully challenge humans, it must often be given significant advantages, such as luck bonuses, access to extra in-game resources or knowledge of the entire game state. Players often proclaim their dislike of these flaws, discussing nonsensical moves AI opponents have made, or the fact that the AI ‘cheats’ — out of necessity, as creating competent strategy game AI on consumer hardware is incredibly difficult, even with state-of-the-art techniques. Therefore, this thesis asks: what frustrates players about the opponents — human and AI — that they play against? By asking this, we can establish the most efficient ways to improve player experience when facing AI opponents. To answer, we explore the computer science that drives AI, the psychology that drives players, and the nature of game interactivity as a whole. Flaws in a range of popular strategy games were investigated, forming a grounded theory on how AI play typically annoys strategy game players. We find that players expect their opponents to conform to a set of expectations. Two scenarios were crafted for an existing strategy game. A mix of qualitative and quantitative methods were used to evaluate how players’ experience of one of those expectations — tension — changes under different, controlled conditions. We find that tension can be observed, and is connected to both player uncertainty and perceptions of power. In addition, analysis of player experiences allowed extraction of practical, concrete methods with which game developers can directly influence player experiences of tension in-game. A further experiment clarifies that investment is also connected to tension, but that it is more effective to phrase it as need when questioning players about their investment in a given objective. It also demonstrates that too little information given to players can remove the connection between perceived powers and tension. Finally, we connect our findings to the current literature on player experience in games, and highlight where further work needs to be done

Design revolutions: IASDR 2019 Conference Proceedings. Volume 4: Learning, Technology, Thinking

In September 2019 Manchester School of Art at Manchester Metropolitan University was honoured to host the bi-annual conference of the International Association of Societies of Design Research (IASDR) under the unifying theme of DESIGN REVOLUTIONS. This was the first time the conference had been held in the UK. Through key research themes across nine conference tracks – Change, Learning, Living, Making, People, Technology, Thinking, Value and Voices – the conference opened up compelling, meaningful and radical dialogue of the role of design in addressing societal and organisational challenges. This Volume 4 includes papers from Learning, Technology and Thinking tracks of the conference

The Art of Adaptation in Film and Video Games

This Special Issue of Arts explores the art and practice of adaptation in several different mediums with a focus on film and video games. The topics covered include experimental game design, narrative design, film and trauma, games adapted from literature, video game cinema, film and the pandemic, film and the environment, film and immigration, and film and culture