Educator Perceptions of XRAuthor: An Accessible Tool for Authoring Learning Content with Different Immersion Levels

Video games can be a space of healing, entertainment, and socialisation for Lesbian-Gay-Bisexual-Trans-Queer-Intersex-Two-Spirit (LGBTQI2S+), women, Black-Indigenous-People of Colour (BIPOC), and accessible needs gaming players. Unfortunately, the overrepresentation of majority groups within video gaming spaces can overshadow this and even contribute to prejudice against minority groups within these spaces. It is therefore important to discuss the varying theories as to why this type of discrimination occurs within the video gaming space. Video game developers also play a large role in shaping their video games and communities and can contribute to reducing the manner of prejudice these groups face. Similarly, members of minority groups can also contribute to changing the culture of video gaming spaces for the better. This paper aims to highlight the current methods by which minority groups benefit from video games, it then discusses the methods that video game developers are aiming to take in reducing it and it then ends with what video game developers and the community at large could be doing better.

Cooperative AI and AI alignment research are increasingly important fields of study as machine learning models are becoming more prevalent in society. Applications such as self-driving cars, realistic AI in games, and human-AI teams, all require further advancement in cooperative and alignment research before more widespread applications can be achieved. However, research in these fields has typically lagged behind other machine learning applications due to the difficulty of creating models that are robust to and can adapt to novel human partners. We attempt to address this through the creation of a framework that uses Archetypal Analysis, a unique clustering algorithm that finds extremal ‘archetype’ points in a dataset and expresses each other point as a convex combination of these archetypes. This framework creates understandable archetypes of players which a reinforcement learning agent can use to adapt accordingly to unseen partners. We show that this framework not only results in performance comparable to other cooperative benchmark models but also achieves higher levels of perceived cooperativeness without the need for human involvement during the training process. As such, we demonstrate that the use of clustering techniques to better model different types of human behaviour and strategies can be an effective approach in improving the ability of AI models to adapt to and improve cooperation with novel partners.

Spiking Neural Networks are increasingly drawing interest due to their potential for large efficiency gains when used with neuromorphic computers. However, when attempting to replicate the successes of Artificial Neural Networks, challenges are faced due to their vastly different architectures and therefore differing methods for training and optimisation. There has been minimal analysis of the differences between encoding and decoding methods and the effect of state space exposure periods on the performance of these networks. The core contribution of this paper is the detailed analysis of decoding methods, state exposure periods, and a learned input encoding method of an evolved Spiking Neural Network within the Reinforcement Learning context. This is demonstrated using the Cart Pole and Lunar Lander Reinforcement Learning problems. The paper discovers a negative correlation between the generation to reach the goal and the state space exposure period over all decoding methods tested. The state exposure period is also found to influence the number of random actions taken due to the decoding methods being unable to select an action. This paper explores the differences in temporal and rate-based decoding as well as identifying benefits in resetting networks to their default states between episode steps. Additionally, the novel input encoder, is effective at pre-processing state information using the same evolutionary algorithm as the rest of the network.

This paper provides a review of the interactive drama field, attempting to create a taxonomy for classifying system affordances for emergence and authorial control, referred to in this paper as "agency architecture". The classification of interactive drama systems according to a spectrum of agency architectures, helps to identify open questions in the field, providing a better understanding of which architectures would benefit from greater attention from the research community. In this paper, several key interactive drama systems from the field's literature are categorised by agency architecture. This is followed by a summary and analysis of their architectural classifications, alongside justifications for their assigned categories. It is then concluded with the identification of a number of research gaps, revealed by the compiled classifications, that highlight potential future research questions.

Research on the use of digital games for cancer patients suggests positive impact in the form of the reduction of depressive symptoms, anxiety, and the feeling of nausea after chemotherapy treatment. This can take the childs focus off their condition and their treatment process and direct it toward other aspects of their childhood. In this article, a comprehensive review of the current literature was conducted to assess how serious games could positively impact paediatric cancer patients. Inclusion criteria were used during data extraction to find the most relevant literature, including the need for a game prototype to have been developed and for the game to specifically target children with cancer as a target audience. Data were extracted including age ranges, treatment and procedure plan, time context, users, purpose, and technology. The resulting serious games were grouped based on their purpose and were classified in three main categories; motivation, education, and distraction. This review demonstrates the positive use of serious games as an intervention for paediatric cancer patients that undergo treatment in hospital. The results suggest that the design of these serious games should consider the purpose of the game within the treatment plan of target audience; the accessibility and suitability of the technology used for the game; and social connection during play.

Theme parks visits can be very playful events for families, however, waiting in the ride’s queues can often be the cause of great frustration. We developed a novel augmented reality game to be played in the theme park’s queue, and an in-the-wild study with X participants using log data and interviews demonstrated that every minute playing was perceived to the same extent of about 5 minutes of not playing the game. We articulate a design space for researchers and strategies for game designers aiming to reduce perceived waiting time in queues. With our work, we hope to extend how we use games in everyday life to make our lives more playful.

We present an approach to procedurally generate the narrative of a simple murder mystery. As a basis for the simulation, we use a rule evaluation system inspired by Ceptre, which employs linear logic to resolve valid actions during each step of the simulation. We extend Ceptre's system with a concept of believable agents to make consecutive actions appear to have a causal connection so that players can comprehend the flow of events. The parts of the generated narratives are then presented to a player whose task it is to figure out who the murderer in this story could have been. Rather than aiming to replace highly authored narratives, this project generates puzzles, which may contain emerging arcs of a story as perceived by the player. While we found that even a simple rule set can create stories that are interesting to reason about, we expect that this type of system is flexible enough to create considerably more engaging stories if enough time is invested in authoring more complex rule sets.

Reinforcement learning (RL) is a machine learning paradigm behind many successes in games, robotics, and control applications. RL agents improve through trial-and-error, therefore undergoing a learning phase during which they perform suboptimally. Research effort has been put into optimizing behavior during this period, to reduce its duration and to maximize after-learning performance. We introduce a novel algorithm that extracts useful information from expert demonstrations (traces of interactions with the target environment) and uses it to improve performance. The algorithm detects unexpected decisions made by the expert and infers what goal the expert was pursuing. Goals are then used to bias decisions while learning. Our experiments in the video game Pac-Man provide statistically significant evidence that our method can improve final performance compared to a state-of-the-art approach.

Q-learning associates states and actions of a Markov Decision Process to expected future reward through online learning. In practice, however, when the state space is large and experience is still limited, the algorithm will not find a match between current state and experience unless some details describing states are ignored. On the other hand, reducing state information affects long term performance because decisions will need to be made on less informative inputs. We propose a variation of Q-learning that gradually enriches state descriptions, after enough experience is accumulated. This is coupled with an ad-hoc exploration strategy that aims at collecting key information that allows the algorithm to enrich state descriptions earlier. Experimental results obtained by applying our algorithm to the arcade game Pac-Man show that our approach significantly outperforms Q-learning during the learning process while not penalizing long-term performance.

In this paper, we propose the strategy of integrating multiple evolutionary processes for personalized procedural content generation (PCG). In this vein, we provide a concrete solution that personalizes game maps in a top-down action-shooter game to suit an individual player's preferences. The need for personalized PCG is steadily growing as the player market diversifies, making it more difficult to design a game that will accommodate a broad range of preferences and skills. In the solution presented here, the geometry of the map and the density of content within that geometry are represented and generated in distinct evolutionary processes, with the player's preferences being captured and utilized through a combination of interactive evolution and a player model formulated as a recommender system. All these components were implemented into a test bed game and experimented on through an unsupervised public experiment. The solution is examined against a plausible random baseline that is comparable to random map generators that have been implemented by independent game developers. Results indicate that the system as a whole is receiving better ratings, that the geometry and content evolutionary processes are exploring more of the solution space, and that the mean prediction accuracy of the player preference models is equivalent to that of existing recommender system literature. Furthermore, we discuss how each of the individual solutions can be used with other game genres and content types.

This paper demonstrates an approach to arranging content within maps of an action-shooter game. Content here refers to any virtual entity that a player will interact with during game-play, including enemies and pick-ups. The content layout for a map is indirectly represented by a Compositional Pattern-Producing Networks (CPPN), which are evolved through the Neuroevolution of Augmenting Topologies (NEAT) algorithm. This representation is utilized within a complete procedural map generation system in the game PCG: Angry Bots. In this game, after a player has experienced a map, a recommender system is used to capture their feedback and construct a player model to evaluate future generations of CPPNs. The result is a content layout scheme that is optimized to the preferences and skill of an individual player. We provide a series of case studies that demonstrate the system as it is being used by various types of players.

This paper provides a review of existing approaches to using evolutionary algorithms (EA) during procedural terrain generation (PTG) processes in video games. A reliable PTG algorithm would allow game maps to be created partially or completely autonomously, reducing the development cost of a game and providing players with more content. Specifically, the use of EA raises possibilities of more control over the terrain generation process, as well as the ability to tailor maps for individual users. In this paper we outline the prominent algorithms that use EA in terrain generation, describing their individual advantages and disadvantages. This is followed by a comparison of the core features of these approaches and an analysis of their appropriateness for generating game terrain. This survey concludes with open challenges for future research.

Procedurally generating content for video games is gaining interest as an approach to mitigate rising development costs and meet users' expectations for a broader range of experiences. This paper explores the use of evolutionary algorithms to aid in the content generation process, especially the creation of three-dimensional terrain. We outline a prototype for the generation of in-game terrain by compiling smaller height-map patches that have been extracted from sample maps. Evolutionary algorithms are applied to this generation process by using crossover and mutation to evolve the layout of the patches. This paper demonstrates the benefits of an interactive two-level parent selection mechanism as well as how to seamlessly stitch patches of terrain together. This unique patch-based terrain model enhances control over the evolution process, allowing for terrain to be refined more intuitively to meet the user's expectations.