Virtual Reality (VR) serious games offer significant potential for augmenting existing methods in medical education and therapy. Despite their advantages, widespread adoption in healthcare remains limited due to the substantial expertise, time, and financial investment required for development. In this paper, we introduce VIA-VR, a novel platform that empowers healthcare professionals to create immersive VR serious games by enabling collaborative workflows where software engineers develop reusable building blocks that healthcare experts can assemble into tailored VR experiences. Our work presents both the platform's concept, including the idea, workflow, and architecture, and the initial implementation that can be built upon. In particular, VIA-VR targets applications where a trainee or patient is immersed in a virtual environment (VE) using a consumer-grade head-mounted display (HMD), and a supervisor monitors the session-often the same individual who has authored or adapted the VR experience. To minimize development efforts and ensure long-term extensibility of the provided functionalities, we combine established software solutions with new domain-specific modules. VIA-VR's features include a VE editor, a story editor, a polygon reduction tool for geometry optimization, a behavior editor based on building blocks (eliminating the need for scripting), a photogrammetry-driven virtual character creation tool, and a physiological data measurement engine. We present two minimal demos that have been created with the first iteration of the platform: a VR exposure therapy application for pyrophobia and a gait rehabilitation exercise application. As an open-source platform, VIA-VR strives to contribute to open standards for developing medical VR serious games, aiming to accelerate innovation and improve accessibility in healthcare.

Dynamic Difficulty Adjustment (DDA) aims to personalize game challenge by adapting AI behavior to player skill, supporting engagement and sustained motivation. In this work, we present a population-based evaluation framework for DDA in Repeated Rock-Paper-Scissors. Agents are trained against a diverse population of rule-based bots to achieve a prescribed win-loss-draw distribution. Experimental results demonstrate that population-based RL enables agents to closely match target outcome distributions and generalize well to novel opponents, illustrating the promise of our approach for scalable, robust DDA.

Games are more than systems to master; they are engines of iterative thought. Through play, users engage in a retroductive loop: an unexpected event triggers hypothesis formation (abduction), leading to action planning and execution (deduction) and learning from feedback (induction). While it is central to player experience, current game AI typically optimizes for challenge balancing or content generation, rarely reflecting back on the player's evolving mental model. We propose treating both the player and the game as coupled retroductive agents that continuously observe, hypothesize, act, and learn across nested timescales. We formalize the retroductive processes of each agent and model their co-evolutionary dialogue as an exchange of inferences and feedback, positioning the game not as a reactive system but as a thinking partner. Building on this formalism, we articulate the vision of retroductive play: Gameplay as an epistemic negotiation in which the system deliberately probes, challenges, and reshapes the player's mental model while refining its own, judging progress by shifts in understanding rather than optimized performance. Retroductive play invites players-and the game that serves them-to respawn their ways of thinking.

Playing Luigi in the original Super Mario Bros feels completely different to playing Mario. Why is that so? We propose an analytical way to design movement in 3D Jump’n’Runs. To this end, we analyzed the movement parameters of three influential games in the genre-Super Mario 64, Banjo Kazooie and Donkey Kong 64-and visualized their evolution with graphs. To compare the findings and further explore the parameter space, we created our own 3D Jump’n’Run game in Unreal Engine 5 supporting a broad set of movement mechanics. Constrained by the concrete movement parameters, we procedurally generate levels by means of Blender’s Geometry Nodes and evaluate the resulting flows of movement in a preliminary study. We believe this approach achieves a high control and explainability for movement mechanics and level design.

Match-3 games are a hugely popular genre of video games, with games like Candy Crush or Royal Match getting millions of downloads and generating revenues of multiple million U.S. dollars per month. These games come in many different forms, with different playstyles and core mechanics, but all share the basic goal of matching pieces to make them disappear. In this paper, a toolkit for the creation of Match-3 games using the Godot Engine is presented, implementing a variety of common mechanics found in many such games. This toolkit was used to conduct a study on the difficulty arising from the deployment of different mechanics. The results are presented and discussed to gain insights into the level design tendencies of Match-3 Games. It is found that games using the swapping control type are generally more difficult than games that use the collapse control type, as well as that the number of special pieces used, influences the difficulty, and a lower amount of special pieces should be used in earlier levels to properly balance the difficulty of these games.

This paper introduces BrainBuilder, a Virtual Reality (VR) serious game designed to support learning about insect neuroanatomy as part of an undergraduate neurobiology curriculum. Leveraging a cognition-oriented design, we used the revised Bloom’s taxonomy to translate complex learning objectives into specific cognitive instructions, subsequently implemented through various engaging game mechanics. The learning scenarios include a shooting game for brain structure identification, a whacka-mole game for naming brain parts, a throwing game for understanding brain regions, and a puzzle game for assembling brain components. The development of BrainBuilder follows a user-centered approach, incorporating formative evaluations to refine gameplay and instructional content.

Creators of digital artifacts are increasingly competing with generative artificial intelligence. Next to common plagiarism, this is another reason why proving one’s authorship becomes increasingly important. One way to support this proof is to document the work process in a certified artifact. Blockchain technology lends itself to this by generation of a data structure that is intrinsically unchangeable and via strong asymmetric cryptographic algorithms tied to the creator. In this paper, we present a concept to record and validate one’s creative work. To demonstrate its feasibility, we provide a proof of concept implementation that documents and encodes a sculpting process conducted in the open-source modeling software Blender.

In Virtual Reality (VR), visual and auditory sensations are effectively leveraged to create immersive experiences. However, touch is significantly underutilized in immersive media. We enhance the VR image viewing experience by integrating haptic and auditory feedback into 3D environments constructed from immersive media. We address the challenges of utilizing depth maps from various image formats to create intractable environments. The VR experience is enhanced using vibrohaptic feedback and audio cues triggered by controller collisions with haptic materials.

We survey the state-of-the-art in procedural generation of buildings (PGB). We analysed the design choices, capabilities and limitations of 71 PGB approaches. We structured our analysis based on a taxonomy of procedural content generation (PCG) that we adapted to PGB by adding relevant details. Overall, we identified 13 taxonomical aspects which we organised in three groups: 1)Generative Capabilities, 2)Accessibility Aspectsand 3)Technological Aspectsof the implementation. It is shown that the generation of different parts of buildings is easiest to accomplish with different PGB techniques. While some approaches built on other ones or even integrate two or more of them to arrive at solutions with a greater effective generative scope, all of them still exhibit rather basic limitations. For instance, the choice of room geometries might be limited to rectangular rooms, they might require numerous manual creation steps, or they might yield potentially invalid output. In addition, only few approaches implement fitting interiors and exteriors which can be used as complete, traversable buildings in virtual environments. Hence, the combination and therefore integration of different approaches for different parts of buildings is a major concern of procedural building generators, rendering their compatibility a characteristic of central importance.

Our paper presents a novel approach to bridge the gap between instructional design and game design concepts in the context of serious games design and analysis. Our solution is to create an ontological mapping between instructional objective types and gameplay goal categories, thereby creating a common language that facilitates better understanding and integration of each other’s capabilities to design and analyse serious games. The paper illustrates the steps towards this mapping and the potential benefits of bridging instructional and game design with ontologies.

This paper presents Baked Burger Bash, a serious game that aims to enhance a drug prevention program for adolescents using virtual reality (VR). The game is based on a simulation of acute cannabinoid intoxication embedded in an interactive cooking game. The player takes on the role of a food truck chef who needs to serve up the orders of passers-by. Various effects of intoxication make it hard to live up to the customers’ expectations. In this paper, we present the scientific background, insights from formative playtests, and feedback from a drug prevention expert who guided the iterative development.

Reinforcement learning (RL) is an adaptive process where an agent relies on its experience to improve the outcome of its performance. It learns by taking actions to maximize its rewards, and by minimizing the gap between predicted and received rewards. In experimental neuropsychology, RL algorithms are used as a conceptual basis to account for several aspects of human motivation and cognition. A number of neuropsychological experiments, such as reversal learning, sequential decision-making, and go-no-go tasks, are required to validate the decisive RL algorithms. The experiments are conducted in digital environments and are comprised of numerous trials that lead to participants’ frustration and fatigue. This paper presents a gamification framework for reinforcement-based neuropsychology experiments that aims to increase participant engagement and provide them with appropriate testing environments.

A modular game engine facilitates development of game technologies and game contents. The latter includes content creation by the player in the form of modding. Based on a requirements analysis to meet the needs of developers, gamers and modders, we present a novel concept for a fully modular game engine. This concept includes a module system, which allows additional code to be loaded at runtime, and various support systems that simplify the use of the engine and the creation of modules. We present a proof-of-concept implementation, underline its fulfillment of the unearthed requirements, and demonstrate its use.

Although virtual reality (VR) applications, especially training environments are booming, and research is advancing, there is still a lack of easy, universally applicable workflows for VR development. To facilitate this process, we present a VR motion data pipeline. Stepping through its phases involves recording motion, storing, and refining the data through runtime evaluation. A generic representation of the motion data allows, for instance, to create automatic integration tests by simulating a variety of different players through data interpolation, or to standardize the setup of psychomotor training tasks by guiding the user to follow the predefined motion path and thereby moving objects in VR. In this paper, we elaborate on the pipeline and demonstrate its application in two examples.

In contrast to neural networks, learning classifier systems are no “black box” algorithm. They provide rule-based artificial intelligence, which can easily be analysed, interpreted and even adapted by humans. We constructed an agent based on such a learning classifier system and trained it to play in a competitive snake environment by utilizing reinforcement learning and self-play methods. Our preliminary experiments show promising results that we plan to extend on in the future.

In this paper we present a requirement analysis of inventory systems for virtual reality (VR) games as well as three according, integrative prototypes, and we compare them to state-of-the-art approaches. Our key contribution integrates solutions for selection and manipulation of inventory items as required by various categories of adventure games. Therefore, we follow a user-centered engineering approach to VR inventory systems design. We further improved the prototypes implementing proprioceptive interactions and adapting established user interface design practices such as the application of layout patterns.

Even though companies store large amounts of business data in enterprise resource planning (ERP) systems, obtaining data for financial fraud detection is prohibitively difficult due to privacy concerns and companies protecting trade secrets. One possible solution is game-based generation of synthetic ERP data, which poses the challenge of designing an environment that generates realistic ERP data and allows players to commit many different types of fraud. In this work, we design a multiplayer game that allows players to cooperatively run a fictional company, while simultaneously challenging them to maximize their personal gain. We introduce an approach for letting players explore fraud scenarios through emergent gameplay and present a prototype that may be primed with information from real world ERP systems to generate realistic data.

This paper aims at showing how different game engines support modding. To this end, the concepts of modding and game engines alongside their historical evolution are introduced first. Additionally, some well-known game engines and mods are presented, and their unique features are described. Further, the effects of using game engines and those that modding has on games, developers, and players are laid out. Particular focus is placed on the effects on competitive games. These effects include an enhanced lifespan for games and the requirement for more development effort. Finally, the modding-support of selected game engines is illustrated and compared with each other. These engines are the id Tech engine(s), Unreal Engine, the Source engine, and Unity. The paper is then concluded with an outlook on future research possibilities and recommendations to developers.

In this demo paper, we present the design of a virtual reality (VR) first-person shooter (FPS) in which the player fends off waves of hostile flying swarm robots that took over the Earth. The purpose of this serious game is to train the player in understanding networks by learning how to dismantle them. We explain the play and game mechanics and the level designs tailored to provide an engaging experience and to re-enforce the network perspective of the swarm dynamics.

There are uncountable things to be remembered, but most people were never shown how to memorize effectively. With this paper, the application The Art of Loci (TAoL) is presented, to provide the means for efficient and fun learning. In a virtual reality (VR) environment users can express their study material visually, either creating dimensional mind maps or experimenting with mnemonic strategies, such as mind palaces. We also present common memorization techniques in light of their underlying pedagogical foundations and discuss the respective features of TAoL in comparison with similar software applications.

In this paper, we introduce a novel video game concept called Emergent Multiplayer Games (EMGs). It aims at providing a platform for large crowds that tune in live game streams. EMGs offer an emergent game experience by empowering a large set of individual players to influence the course of a single, collectively played game. We introduce the general concept of EMGs and present our approach to an EMG prototype. Our implementation is built for audiences of varying numbers and diverse personas. It features a control system based on multiple forms of voting ballots. A preliminary evaluation of the prototype helped to identify EMG designs effective in terms of usability and fun, but also the relationships of our implemented game mechanics and game control system in terms of information flow and balancing.

Navigation is considered one of the most fundamental challenges in Virtual Reality (VR) and has been extensively researched [11]. The world-in-miniature (WIM) navigation metaphor allows users to travel in large-scale virtual environments (VEs) regardless of available physical space while maintaining a high-level overview of the VE. It relies on a hand-held, scaled-down duplicate of the entire VE, where the user’s current position is displayed, and an interface provided to introduce his/her next movements [17]. There are several extensions to deal with challenges of this navigation technique, e.g. scaling and scrolling [23]. In this work, a WIM is presented that integrates state-of-the-art research insights and incorporates additional features that became apparent during the integration process. These features are needed to improve user interactions and to provide both look-ahead and post-travel feedback. For instance, a novel occlusion handling feature hides the WIM geometry in a rounded space reaching from the user’s hand to his/her forearm. This allows the user to interact with occluded areas of the WIM such as buildings. Further extensions include different visualizations for occlusion handling, an interactive preview screen, post-travel feedback, automatic WIM customization, a unified diegetic UI design concerning WIM and user representation, and an adaptation of widely established gestures to control scaling and scrolling of the WIM. Overall, the presented WIM design integrates and extends state-of-the-art interaction tasks and visualization concepts to overcome open conceptual gaps and to provide a comprehensive practical solution for traveling in VR.

This paper presents a new approach of heightmap generation for Real-Time Strategy games (RTS) based on Cellular Automata (CA) in the context of various established techniques. The proposed approach uses different CA rulesets to generate and modify maps for the RTS game Supreme Commander. To evaluate the quality of the generated maps, a survey was conducted asking 30 participants about map quality compared to user-generated maps. The participants rated the maps more balanced and novel but less aesthetically pleasing. The paper concludes with according future work propositions to improve the presented approach.

Single crystal structure determination is the foremost method to determine atomic structures – from minerals to viruses. However, it is a complex process in which errors do not only lead to flawed structures, but may also hinder structure solution entirely. Many of these errors can be recognized by visualizing the measured diffraction data in reciprocal space. Here, we present an immersive tool to support such an analysis. We aim to supplement this traditionally 2D desktop-based investigation of 3D diffraction data with the strengths of immersive visualization, especially depth perception and spatially tracked input devices.

Recent improvements in virtual reality (VR) technology promise the opportunity to redesign established game genres, such as real-time strategy (RTS) games. In this work we have a look at a taxonomy of RTS games and apply it to RTS titles for VR. Hereby, we identify possible difficulties such as the need for novel means of navigation in VR. We discuss conceivable solutions and illustrate them by referring to relevant work by others and by means of AStar0ID, an exploratory prototype VR RTS science fiction game. Our main contribution is the systematic inspection and discussion of foundational RTS aspects in the context of VR and, thus, to provide a substantial basis to further rethink and evolve the RTS genre in this new light.

We compared three techniques for handling head-object collisions in room-scale virtual reality (VR). We developed a game whose mechanics induce such collisions which we either addressed (1) not at all, (2) by fading the screen information to black, or (3) by restricting translation, i.e. correcting the virtual offset in such a way that no penetration occurred. We measured these conditions' impact on simulator sickness, fun, and immersion perception. We found that the translation-restricted method yielded the greatest immersion value but also contributed the most to simulator sickness.

Continued development and maintenance of software requires understanding its design and behavior. Software at runtime creates a complex network of call--callee relationships that are hard to determine but that developers need to understand to optimize software performance. Existing tools typically focus on static aspects (e.g., Structure101 or SonarQube), or they are difficult to use and require high expertise (e.g., software profiling tools).

In this paper, we present a realtime fluid simulation based on cellular automata (CAs). The main goal is to demonstrate the performance and extensibility of this approach. To show this, we created a fluid simulation and extended it by simulating different kinds of fluids at the same time with effects like oil foating on water and a focus on short computation time. This makes the simulation interesting for games in VR. In our simulation, we had a high framerate of 100 FPS for a CA running on the CPU with 1763 cells due to parallelization and optimization.

Swarm-based braiding of structures represents a novel research direction in the domain of building architecture. The idea is that autonomous agents, for instance robots that unroll threads or plants that grow, are programmed or influenced to braid. It is an aspect of biohybrid systems where organisms and robots join forces. In order to harness this idea, we have developed a swarm-based model that allows architects to explore the resulting design spaces in virtual reality. In this paper, we present (1) the model of our swarm-based simulation that aims at growing braided structures, (2) the design elements to guide the otherwise self-organising virtual agents, and (3) the user interface that allows the user to configure, place and grow the swarms of braiding agents. We also present results of a first user study with students and faculty from architecture, in which we tried to capture the usability of our first prototype based on a survey and an analysis of the built results.

Distributed self-organising systems often face conflicts if more than one entity tries to access a limited resource. In order to solve this conflict, research focuses on techniques for resource allocation considering different priorities. In this paper, we propose to tackle the decision problem of whom to assign the resource by means of a co-evolutionary approach. We investigate appropriate fitness estimations, representation schemes, and configuration of the underlying genetic operators. We demonstrate the convergence and efficiency of our approach using an exemplary system model.

Robot Gardens are an augmented reality concept allowing a human user to design a biohybrid, plant-robot system. Plants growing from deliberately placed seeds are directed by robotic units that the user can position, configure and activate. For example, the robotic units may serve as physical shields or frames but they may also guide the plants' growth through emission of light. The biohybrid system evolves over time to redefine architectural spaces. This gives rise to the particular challenge of designing a biohybrid system before its actual implementation and potentially long before its developmental processes unfold. Here, an augmented reality interface featuring according simulation models of plants and robotic units allows one to explore the design space a priori. In this work, we present our first functional augmented reality prototype to design biohybrid systems. We provide details about its workings and elaborate on first empirical studies on its usability.

In this paper, we present our efforts towards creating a deliberately sickening virtual reality (VR) game. Based on a considerable number of tests, we can show that games can be enjoyable despite experienced adverse effects that arise from frequent acceleration in VR and additional post-processing distortions of the rendered scene. We briefly explain the rationale that drove us to take these measures, details about their realisation and results from a questionary-based user evaluation.

Network protocols are deployed in highly dynamic environments, but typically configured with a static setup of configurations. The Organic Network Control system (ONC) has been developed to alter protocol configurations at runtime. ONC is equipped with online learning capabilities and safety considerations. This paper presents a first TCP-based study on how this approach can be applied to end-to-end protocols and simultaneously alleviating the drawbacks of a simulation-based optimisation procedure. The paper explains the developed algorithm and demonstrates the benefit of the solution in an Omnet++ scenario.

In this paper we present an approach to designing swarms of autonomous, adaptive robots. An observer/controller framework that has been developed as part of the Organic Computing initiative provides the architectural foundation for the individuals' adaptivity. Relying on an extended Learning Classifier System (XCS) in combination with adequate simulation techniques, it empowers the individuals to improve their collaborative performance and to adapt to changing goals and changing conditions. We elaborate on the conceptual details, and we provide first results addressing different aspects of our multi-layered approach. Not only for the sake of generalisability, but also because of its enormous transformative potential, we stage our research design in the domain of quad-copter swarms that organise to collaboratively fulfil spatial tasks such as maintenance of building facades. Our elaborations detail the architectural concept, provide examples of individual self-optimisation as well as of the optimisation of collaborative efforts, and we show how the user can control the swarm at multiple levels of abstraction. We conclude with a summary of our approach and an outlook on possible future steps.

In this paper, we present a self-organized approach to automatically identify and create hierarchies of cooperative agents. Once a group of cooperative agents is found, a higher order agent is created which in turn learns the group behaviour. This way, the number of agents and thus the complexity of the multiagent system will be reduced, as one agent emulates the behaviour of several agents. Our proposed method of creating hierarchies captures the dynamics of a multiagent system by adaptively creating and breaking down hierarchies of agents as the simulation proceeds. Experimental results on two MAPK signaling pathways suggest that the proposed approach is suitable in stable systems while periodic systems still need further investigations.

For the purpose of medical education, our research team is creating LINDSAY, a 3-dimensional, interactive computer model of male and female anatomy and physiology. As part of the LINDSAY-Virtual Human project, we have developed a component-based computational framework that allows the utilization of various formal representations, computation engines and visualization technologies within a single simulation context. For our agent-based simulations, the graphics, physics and behaviours of our interacting entities are implemented through a set of component engines. We have developed a light-weight client/server component, which spreads its siblings in the system's component hierarchy over a wireless or wired network infrastructure. In this paper we demonstrate how our client/server component paves new ways for organizing, generating, computing and presenting educational contents.

In this paper we present a swarm grammar system that makes use of bio-inspired mechanisms of reproduction, communication and construction in order to build three-dimensional structures. Ultimately, the created structures serve as idea models that lend themselves to inspirations for architectural designs.

We recently introduced swarm grammars as an extension of Lindenmayer systems to model dynamic growth processes in 3D space through a large number of interacting (swarm) agents. Grammatical rewrite rules define different types of agents and their evolution over time. Sets of parameters determine specific interaction behaviors among the generated swarms. As we will show, swarm grammars lend themselves to creating an ecology of interacting entities and dynamic structures that are built by a multitude of agents. In addition to a rather traditional approach of evolving swarm grammars through interactive genetic programming, we explore new ways of designing ecologies of swarm agents by immersing the breeder into the growth and evolution processes. The system designer takes on the role of a 'tinkerer' or 'gardener', who is equipped with tools to influence and shape the on-going growth, evolutionary, and other dynamic processes within the swarm grammar ecology. Spatial genetic operators can be directed to specific locations within the evolving swarms. This enables the breeder to overview large numbers of phenotypic developmental processes and implicitly direct their evolution.

This paper presents an approach to exploit free text descriptions of TV programmes as available from EPG data sets for a TV recommender system that takes the content of programmes into account. The paper focuses on the natural language understanding problem underlying the analysis of free text descriptions and on methods of classifying free text descriptions with respect to a natural language user query. We close with an evaluation of user acceptance and a discussion of future work.

The complex interactions of natural swarms, for example formed by some social insects, are difficult to comprehend. Considering tasks such as nest-building, the necessary underlying communication presumably happens indirectly by changing and reacting on the environment. This paper presents an overall approach to interactively evolve rule-based swarms that create three-dimensional structures in continuous space. The approach comprises the design of the swarm agent, details about the breeding process and first results. A swarm is determined by a set of flocking parameters and a set of instructional rules that allow the agents to change their local structural environment. The center or focus of the swarm's endeavour may be shifted either on a swarm agent or on a fixed point in space. The alteration of the supplied 3D structure during the course of evolution enables an external supervisor to interactively guide the development of a swarm.