--This paper proposes a structured methodology to evaluate AI-generated game narratives, leveraging the Delphi study structure with a panel of narrative design experts. Our approach synthesizes story quality dimensions from literature and expert insights, mapping them into the Kano model framework to understand their impact on player satisfaction. The results can inform game developers on prioritizing quality aspects when co-creating game narratives with generative AI. While generative AI has surged into public and research consciousness following the release of systems like ChatGPT, video games have a longer tradition of using AI techniques to generate content that would otherwise be authored by human designers. This tradition is well established in the field of Procedural Content Generation, which encompasses a range of methods for algorithmically creating game elements such as levels, characters, quests, and storylines [1].

Modern commercial games are designed for mass appeal, not for individual players, but there is a unique opportunity in video games to better fit the individual through adapting game elements. In this paper, we focus on AI Directors, systems which can dynamically modify a game, that personalize the player experience to match the player's preference. In the past, some AI Director studies have provided inconclusive results, so their effect on player experience is not clear. We take three AI Directors and directly compare them in a human subject study to test their effectiveness on quest selection. Our results show that a non-random AI Director provides a better player experience than a random AI Director.

This paper surveys the current state of the art in affective computing principles, methods and tools as applied to games. We review this emerging field, namely affective game computing, through the lens of the four core phases of the affective loop: game affect elicitation, game affect sensing, game affect detection and game affect adaptation. In addition, we provide a taxonomy of terms, methods and approaches used across the four phases of the affective game loop and situate the field within this taxonomy. We continue with a comprehensive review of available affect data collection methods with regards to gaming interfaces, sensors, annotation protocols, and available corpora. The paper concludes with a discussion on the current limitations of affective game computing and our vision for the most promising future research directions in the field.

AI's presence in the gaming industry has evolved from a mere novelty to an indispensable force. With every algorithmic breakthrough, new possibilities and challenges arise for gamers and developers alike. In March 2023, a Reddit user shared a story of how AI was being used where she worked. "I lost everything that made me love my job through Midjourney overnight," the author wrote. The post got a lot of attention, and its author agreed to talk to WIRED on condition of anonymity, out of fear of being identified by her employer.

Art and technology have a unique intersection that has been explored in various fields, ranging from film and music to video games and gambling. With the rise of digital technology, art has found a new medium to explore and experiment with. The world of digital creativity has opened up endless possibilities for artists and creatives, allowing them to push the boundaries of what was previously thought possible. One field where this intersection has had a significant impact is gambling. Gambling has evolved from simple card games to a billion-dollar industry with the rise of online casinos and mobile apps. The gambling industry has been quick to embrace technology, and as a result, we have seen an explosion in the number of games and betting options available.

In many real-world situations, data is distributed across multiple self-interested agents. These agents can collaborate to build a machine learning model based on data from multiple agents, potentially reducing the error each experiences. However, sharing models in this way raises questions of fairness: to what extent can the error experienced by one agent be significantly lower than the error experienced by another agent in the same coalition? In this work, we consider two notions of fairness that each may be appropriate in different circumstances: "egalitarian fairness" (which aims to bound how dissimilar error rates can be) and "proportional fairness" (which aims to reward players for contributing more data). We similarly consider two common methods of model aggregation, one where a single model is created for all agents (uniform), and one where an individualized model is created for each agent. For egalitarian fairness, we obtain a tight multiplicative bound on how widely error rates can diverge between agents collaborating (which holds for both aggregation methods). For proportional fairness, we show that the individualized aggregation method always gives a small player error that is upper bounded by proportionality. For uniform aggregation, we show that this upper bound is guaranteed for any individually rational coalition (where no player wishes to leave to do local learning).

The gaming industry is constantly evolving, and the use of artificial intelligence (AI) is rapidly increasing in gaming. AI applications in the gaming industry have become an essential part of the gaming experience. It is providing more immersive and realistic gameplay. In this blog, we will discuss some of the most important AI applications in the gaming industry. Non-Player Characters (NPCs): One of the most common AI applications in the gaming industry is the use of AI to create non-player characters (NPCs).

Player modelling is the field of study associated with understanding players. One pursuit in this field is affect prediction: the ability to predict how a game will make a player feel. We present novel improvements to affect prediction by using a deep convolutional neural network (CNN) to predict player experience trained on game event logs in tandem with localized level structure information. We test our approach on levels based on Super Mario Bros. (Infinite Mario Bros.) and Super Mario Bros.: The Lost Levels (Gwario), as well as original Super Mario Bros. levels. We outperform prior work, and demonstrate the utility of training on player logs, even when lacking them at test time for cross-domain player modelling.

"Do you think a butler is a slave? What is the difference between a butler and a slave?" The above was a calculated, almost chilly response by LaMDA, Google's artificially intelligent chatbot, to a question posed by Google engineer Blake Lemoine. A further exchange only heightened Lemoine's suspicions and, much to the chagrin of Google, concluded that LaMDA is sentient, of having the ability to display a sense of consciousness. While the tech company has sought to distance itself from Lemoine's claims and attempt to debunk them with its own team of ethicists and engineers, the case with LaMDA has brought an uncomfortable spotlight to the world of artificial intelligence (AI).

A film's themes, or its plot, can be misconstrued by a lazy viewer. Only a video game, however, can punish an audience's faults. If a player mistimes a jump, falls to an adversary, or fails to reach the end of a level, a game can deny them access to the rest of the work, halting progress until they pass the test or resign in defeat. The video-game director Hidetaka Miyazaki, who's in his late forties, has punished more players than perhaps anyone else. In Dark Souls, the 2011 fantasy game that made him famous, you play as a loin-clothed wretch, racing through sewers and cowering in forests.