A massive leak from the FTC v. Microsoft court battle showed Microsoft's roadmap for a mid-generation Xbox Series X console, but that wasn't the only news. The same document also revealed Microsoft's tentative plans for the next-generation Xbox -- what it calls a "hybrid game platform." The system would combine local hardware and cloud computing to create an "immersive game & app platform" arriving around 2028, according to a leaked May 2022 presentation hidden inside another PDF. "Our vision: Develop a next generation hybrid game platform capable of leveraging the combined power of the client and cloud to deliver deeper immersion and entirely new classes of game experiences," one of the slides reads. "Optimized for real time game play and creators, we will enable new levels of performance beyond the capabilities of the client hardware alone."

Dynamic Difficulty Adjustment (DDA) is a viable approach to enhance a player's experience in video games. Recently, Reinforcement Learning (RL) methods have been employed for DDA in non-competitive games; nevertheless, they rely solely on discrete state-action space with a small search space. In this paper, we propose a continuous RL-based DDA methodology for a visual working memory (VWM) game to handle the complex search space for the difficulty of memorization. The proposed RL-based DDA tailors game difficulty based on the player's score and game difficulty in the last trial. We defined a continuous metric for the difficulty of memorization. Then, we consider the task difficulty and the vector of difficulty-score as the RL's action and state, respectively. We evaluated the proposed method through a within-subject experiment involving 52 subjects. The proposed approach was compared with two rule-based difficulty adjustment methods in terms of player's score and game experience measured by a questionnaire. The proposed RL-based approach resulted in a significantly better game experience in terms of competence, tension, and negative and positive affect. Players also achieved higher scores and win rates. Furthermore, the proposed RL-based DDA led to a significantly less decline in the score in a 20-trial session.

Matchmaking systems are vital for creating fair matches in online multiplayer games, which directly affects players' satisfactions and game experience. Most of the matchmaking systems largely rely on precise estimation of players' game skills to construct equitable games. However, the skill rating of a novice is usually inaccurate, as current matchmaking rating algorithms require considerable amount of games for learning the true skill of a new player. Using these unreliable skill scores at early stages for matchmaking usually leads to disparities in terms of team performance, which causes negative game experience. This is known as the ''cold-start'' problem for matchmaking rating algorithms. To overcome this conundrum, this paper proposes QuickSKill, a deep learning based novice skill estimation framework to quickly probe abilities of new players in online multiplayer games. QuickSKill extracts sequential performance features from initial few games of a player to predict his/her future skill rating with a dedicated neural network, thus delivering accurate skill estimation at the player's early game stage. By employing QuickSKill for matchmaking, game fairness can be dramatically improved in the initial cold-start period. We conduct experiments in a popular mobile multiplayer game in both offline and online scenarios. Results obtained with two real-world anonymized gaming datasets demonstrate that proposed QuickSKill delivers precise estimation of game skills for novices, leading to significantly lower team skill disparities and better player game experience. To the best of our knowledge, proposed QuickSKill is the first framework that tackles the cold-start problem for traditional skill rating algorithms.

Artificial intelligence is evolving the landscape of every industry, and the gaming industry is no exception. Through innovation and growth, technology is exceeding our expectations every day. In gaming, artificial intelligence (AI) refers to responsive and flexible video game experiences. While artificial intelligence has long been present in video games, it is today seen as a burgeoning new frontier in how games are both created and played. AI games are progressively handing over control of the game experience to the player, whose actions influence the game experience.

In this paper we propose an approach for personalising the space in which a game is played (i.e., levels) dependent on classifications of the user's facial expression -- to the end of tailoring the affective game experience to the individual user. Our approach is aimed at online game personalisation, i.e., the game experience is personalised during actual play of the game. A key insight of this paper is that game personalisation techniques can leverage novel computer vision-based techniques to unobtrusively infer player experiences automatically based on facial expression analysis. Specifically, to the end of tailoring the affective game experience to the individual user, in this paper we (1) leverage the proven InSight facial expression recognition SDK as a model of the user's affective state InSight, and (2) employ this model for guiding the online game personalisation process. User studies that validate the game personalisation approach in the actual video game Infinite Mario Bros. reveal that it provides an effective basis for converging to an appropriate affective state for the individual human player.

Increasingly powered by artificial intelligence, sales in the video gaming industry are anticipated to increase significantly in 2022. If you're a hard-core, veteran gamer, then the recent advances in gaming technology are probably not lost on you. You've likely already seen first-hand how gaming has improved by light years since the old arcade scene. For example, one of the best-selling early video games was Pac-Man. Gamers played Pac-Man on a relic game system known as Atari 2600.

In this project, we designed an intelligent assistant player for the single-player game Space Invaders with the aim to provide a satisfying co-op experience. The agent behaviour was designed using reinforcement learning techniques and evaluated based on several criteria. We validate the hypothesis that an AI-driven computer player can provide a satisfying co-op experience.

RL algorithm learns how to act best through many attempts and failures. Trial-and-error learning is connected with the so-called long-term reward. This reward is the ultimate goal the agent learns while interacting with an environment through numerous trials and errors. The algorithm gets short-term rewards that together lead to the cumulative, long-term one. So, the key goal of reinforcement learning used today is to define the best sequence of decisions that allow the agent to solve a problem while maximizing a long-term reward. And that set of coherent actions is learned through the interaction with environment and observation of rewards in every state. Reinforcement learning is distinguished from other training styles, including supervised and unsupervised learning, by its goal and, consequently, the learning approach. Three ML training styles compared.

RL algorithm learns how to act best through many attempts and failures. Trial-and-error learning is connected with the so-called long-term reward. This reward is the ultimate goal the agent learns while interacting with an environment through numerous trials and errors. The algorithm gets short-term rewards that together lead to the cumulative, long-term one. So, the key goal of reinforcement learning used today is to define the best sequence of decisions that allow the agent to solve a problem while maximizing a long-term reward. And that set of coherent actions is learned through the interaction with environment and observation of rewards in every state. Reinforcement learning is distinguished from other training styles, including supervised and unsupervised learning, by its goal and, consequently, the learning approach. Three ML training styles compared.