Abstract--This paper outlines the design vision and planned evolution of Blexer v3, a modular and AI-driven rehabilitation ecosystem based on serious games. Building on insights from previous versions of the system, we propose a new architecture that aims to integrate multimodal sensing, real-time reasoning, and intelligent control. The envisioned system will include distinct modules for data collection, user state inference, and gameplay adaptation. Key features such as dynamic difficulty adjustment (DDA) and procedural content generation (PCG) are also considered to support personalized interventions. We present the complete conceptual framework of Blexer v3, which defines the modular structure and data flow of the system. This serves as the foundation for the next phase: the development of a functional prototype and its integration into clinical rehabilitation scenarios. Video games have evolved significantly since their inception in the 1960s, becoming a cultural force in the late 1980s and early 1990s [1]. With the growth of the videogame industry, games have expanded into fields such as education, military, and healthcare, known as Serious Games (SGs) [2]. In healthcare, SGs have shown promise in screening [3] and rehabilitation [4].

Balancing game difficulty in video games is a key task to create interesting gaming experiences for players. Mismatching the game difficulty and a player's skill or commitment results in frustration or boredom on the player's side, and hence reduces time spent playing the game. In this work, we explore balancing game difficulty using machine learning-based agents to challenge players based on their current behavior. This is achieved by a combination of two agents, in which one learns to imitate the player, while the second is trained to beat the first. In our demo, we investigate the proposed framework for personalized dynamic difficulty adjustment of AI agents in the context of the fighting game AI competition.

Motivated by applications in electronic games as well as teaching systems, we investigate the problem of dynamic difficulty adjustment. The task here is to repeatedly find a game difficulty setting that is neither'too easy' and bores the player, nor'too difficult' and overburdens the player. The contributions of this paper are (i) the formulation of difficulty adjustment as an online learning problem on partially ordered sets, (ii) an exponential update algorithm for dynamic difficulty adjustment, (iii) a bound on the number of wrong difficulty settings relative to the best static setting chosen in hindsight, and (iv) an empirical investigation of the algorithm when playing against adversaries.

Motivated by applications in electronic games as well as teaching systems, we investigate the problem of dynamic difficulty adjustment. The task here is to repeatedly find a game difficulty setting that is neither too easy' and bores the player, nor too difficult' and overburdens the player. The contributions of this paper are ( i) formulation of difficulty adjustment as an online learning problem on partially ordered sets, ( ii) an exponential update algorithm for dynamic difficulty adjustment, ( iii) a bound on the number of wrong difficulty settings relative to the best static setting chosen in hindsight, and ( iv) an empirical investigation of the algorithm when playing against adversaries.

Virtual Reality (VR) games that feature physical activities have been shown to increase players' motivation to do physical exercise. However, for such exercises to have a positive healthcare effect, they have to be repeated several times a week. To maintain player motivation over longer periods of time, games often employ Dynamic Difficulty Adjustment (DDA) to adapt the game's challenge according to the player's capabilities. For exercise games, this is mostly done by tuning specific in-game parameters like the speed of objects. In this work, we propose to use experience-driven Procedural Content Generation for DDA in VR exercise games by procedurally generating levels that match the player's current capabilities. Not only finetuning specific parameters but creating completely new levels has the potential to decrease repetition over longer time periods and allows for the simultaneous adaptation of the cognitive and physical challenge of the exergame. As a proof-of-concept, we implement an initial prototype in which the player must traverse a maze that includes several exercise rooms, whereby the generation of the maze is realized by a neural network. Passing those exercise rooms requires the player to perform physical activities. To match the player's capabilities, we use Deep Reinforcement Learning to adjust the structure of the maze and to decide which exercise rooms to include in the maze. We evaluate our prototype in an exploratory user study utilizing both biodata and subjective questionnaires.

Three Californians say that the video game publisher Electronic Arts is secretly manipulating them. On Nov. 9, they filed a class-action lawsuit accusing EA of surreptitiously using a patented A.I. technology known as dynamic difficulty adjustment in its FIFA, Madden, and NHL games--three of the biggest sports games on the planet. The lawsuit claims EA is using the technology to unfairly increase the difficulty of multiplayer mode online matches in order to encourage players to spend real-world money to boost their chances of winning. EA has denied ever implementing the technology and has called the lawsuit "baseless." For years, players have been stewing over ideas of fairness and balance in games, feeling taken for granted at best and taken advantage of at worst. The class-action complaint, Zajonc et al. v. Electronic Arts, doesn't contain any evidence for its claim, but that's fairly typical for this sort of class-action complaint.

Videogames are nowadays one of the biggest entertainment industries in the world. Being part of this industry means competing against lots of other companies and developers, thus, making fanbases of vital importance. They are a group of clients that constantly support your company because your video games are fun. Videogames are most entertaining when the difficulty level is a good match for the player's skill, increasing the player engagement. However, not all players are equally proficient, so some kind of difficulty selection is required. In this paper, we will present Dynamic Difficulty Adjustment (DDA), a recently arising research topic, which aims to develop an automated difficulty selection mechanism that keeps the player engaged and properly challenged, neither bored nor overwhelmed. We will present some recent research addressing this issue, as well as an overview of how to implement it. Satisfactorily solving the DDA problem directly affects the player's experience when playing the game, making it of high interest to any game developer, from independent ones, to 100 billion dollar businesses, because of the potential impacts in player retention and monetization.

Methods for dynamic difficulty adjustment allow games to be tailored to particular players to maximize their engagement. However, current methods often only modify a limited set of game features such as the difficulty of the opponents, or the availability of resources. Other approaches, such as experience-driven Procedural Content Generation (PCG), can generate complete levels with desired properties such as levels that are neither too hard nor too easy, but require many iterations. This paper presents a method that can generate and search for complete levels with a specific target difficulty in only a few trials. This advance is enabled by through an Intelligent Trial-and-Error algorithm, originally developed to allow robots to adapt quickly. Our algorithm first creates a large variety of different levels that vary across predefined dimensions such as leniency or map coverage. The performance of an AI playing agent on these maps gives a proxy for how difficult the level would be for another AI agent (e.g. one that employs Monte Carlo Tree Search instead of Greedy Tree Search); using this information, a Bayesian Optimization procedure is deployed, updating the difficulty of the prior map to reflect the ability of the agent. The approach can reliably find levels with a specific target difficulty for a variety of planning agents in only a few trials, while maintaining an understanding of their skill landscape.

Motivated by applications in electronic games as well as teaching systems, we investigate the problem of dynamic difficulty adjustment. The task here is to repeatedly find a game difficulty setting that is neither too easy' and bores the player, nor too difficult' and overburdens the player. The contributions of this paper are ($i$) formulation of difficulty adjustment as an online learning problem on partially ordered sets, ($ii$) an exponential update algorithm for dynamic difficulty adjustment, ($iii$) a bound on the number of wrong difficulty settings relative to the best static setting chosen in hindsight, and ($iv$) an empirical investigation of the algorithm when playing against adversaries. Papers published at the Neural Information Processing Systems Conference.

Dynamic Difficulty Adjustment (DDA) is a mechanism used in video games that automatically tailors the individual gaming experience to match an appropriate difficulty setting. This is generally achieved by removing pre-defined difficulty tiers such as Easy, Medium and Hard; and instead concentrates on balancing the gameplay to match the challenge to the individual's abilities. The work presented in this paper examines the implementation of DDA in a custom survival game developed by the author, namely Colwell's Castle Defence. The premise of this arcade-style game is to defend a castle from hordes of oncoming enemies. The AI system that we developed adjusts the enemy spawn rate based on the current performance of the player. Specifically, we read the Player Health and Gate Health at the end of each level and then assign the player with an appropriate difficulty tier for the proceeding level. We tested the impact of our technique on thirty human players and concluded, based on questionnaire feedback, that enabling the technique led to more enjoyable gameplay.