Video and body cameras are increasingly used by operational personnel, such as first responders, to improve situational awareness and safety of operations. However, to attain the full potential of video applications in this domain, operators need support to find the relevant information in the multiple video streams sent from the accident site. Needed are flexible applications that can handle multiple video streams and augment relevant parts of the video to support the operator. In this thesis, we present a video application, with virtual object augmentation and overlay functionality. The system handles a set of video streams and augments them using Unreal Engine and Gstreamer. Current modern software frameworks for video streaming and augmentation were examined as part of the development.  The performance of the application was evaluated using a simulated set of video streams and augmentation requests. Frames per second were measured to ensure a reliable and functional application. The study suggests that Unreal Engine, together with Gstreamer, is a suitable framework combination for the development of this application. Unreal Engine provides nDisplay, which is a powerful feature for multi-display setups. It supports synchronized presentation on displays, even in a cluster of multiple computers, with easy setup in a nDisplay editor. Furthermore, extending Unreal using C++ facilitates the integration with Gstreamer that enables integration with other applications with its native C++ support and external APIs for accessing media data. The performance measurements of the final application show adequate performance with respect to the defined use cases.

When striving for realism in first-person games, the use of sampled audio has been frequent, and it is often argued that a higher level of auditory authenticity will lead to an increased realism.However, in this paper, it is argued that, for the purpose of realism, attention should instead be directed at making players themselves a more contributing factor to how sound is perceived; the more the audio of a game changes with, or adapts to, player input, the more realistic it should be perceived. This idea is tested in a first-person game where participants play two different levels where one level’s wind sound has audio parameters adapt to the camera’s yaw rotator value, while the other level’s wind sound does not adapt at all. The participants’ experience of each design approach is evaluated using a quantitative and qualitative analysis. Results suggests that an increase of adaptive audio can lead to an increase in perceived realism in first-person games. A difference could clearly be perceived between the approaches and there were indications of preference for the adaptive approach. First-person games, such as simulators, as well as VR games, may well benefit from this approach in that it could increase the perceived realism.

<p dir="ltr">The concept of reinforcement learning has become increasingly relevant in learning- based applications, especially in the field of autonomous navigation, because of its fundamental nature to operate without the necessity of labeled data. However, the infeasibility of training reinforcement learning based autonomous navigation applications in a real-world setting has increased the popularity of researching and developing on autonomous navigation systems by creating simulated environments in game engine platforms. This thesis investigates the comparative performance of Unity and Unreal Engine 5 within the framework of a reinforcement learning system applied to autonomous race car navigation. A rudimentary simulated setting featuring a model car navigating a racetrack is developed, ensuring uniformity in environmental aspects across both Unity and Unreal Engine 5. The research employs reinforcement learning with genetic algorithms to instruct the model car in race track navigation; while the tools and programming methods for implementing reinforcement learning vary between the platforms, the fundamental concept of reinforcement learning via genetic algorithms remains consistent to facilitate meaningful comparisons. The implementation includes logging of key performance variables during run times on each platform. A comparative analysis of the performance data collected demonstrates Unreal Engine's superior performance across the collected variables. These findings contribute insights to the field of autonomous navigation systems development and reinforce the significance of choosing an optimal underlying simulation platform for reinforcement learning applications.</p>

We propose a Model-Based Deep Reinforcement Learning (MBDRL) framework for collaborative paylaod transportation using Unmanned Aerial Vehicles (UAVs) in Search and Rescue (SAR) missions, enabling heavier payload conveyance while maintaining vehicle agility. Our approach extends the single-drone application to a novel multi-drone one, using the Probabilistic Ensembles with Trajectory Sampling (PETS) algorithm to model the unknown stochastic system dynamics and uncertainty. We use the Multi-Agent Reinforcement Learning (MARL) framework via a centralized controller in a leader-follower configuration. The agents utilize the approximated transition function in a Model Predictive Controller (MPC) configured to maximize the reward function for waypoint navigation, while a position-based formation controller ensures stable flights of these physically linked UAVs. We also developed an Unreal Engine (UE) simulation connected to an offboard planner and controller via a Robot Operating System (ROS) framework that is transferable to real robots. This work achieves stable waypoint navigation in a stochastic environment with a sample efficiency following that seen in single UAV work. This work has been funded by the National Science Foundation (NSF) under Award No. 2046770. / Master of Science / We apply the Model-Based Deep Reinforcement Learning (MBDRL) framework to the novel application of a UAV team transporting a suspended payload during Search and Rescue missions. Collaborating UAVs can transport heavier payloads while staying agile, reducing the need for human involvement. We use the Probabilistic Ensemble with Trajectory Sampling (PETS) algorithm to model uncertainties and build on the previously used single UAVpayload system. By utilizing the Multi-Agent Reinforcement Learning (MARL) framework via a centralized controller, our UAVs learn to transport the payload to a desired position while maintaining stable flight through effective cooperation. We also develop a simulation in Unreal Engine (UE) connected to a controller using a Robot Operating System (ROS) architecture, which can be transferred to real robots. Our method achieves stable navigation in unpredictable environments while maintaining the sample efficiency observed in single UAV scenarios.

This paper will explore the possibilities of using an automated self-made procedural tool to create real-time environments based on simple image masks. The purpose of this is to enable a concept artist or level designer to quickly get out results in a game engine and to be able to explore ideas. The goal of this thesis was to better understand how you can break down simple ideas and shapes into more complex details and assets. In the first part of this thesis, I go over the traditional workflow of creating a real-time environment. I then go on and break down my tool, what it does and how it works. I start off with a Photoshop file, make tools in Houdini and then utilize those in Unreal for the end result. I also argument about the time-saving possibilities with these tools. From the work, I draw the conclusion that these kinds of tools save a lot of time for repeating tasks and the creation of similar environments.

Este trabalho investiga a aplicação de rede de comportamentos estendidas ao domínio de jogos de computador. Redes de comportamentos estendidas (RCE) são uma classe de arquiteturas para seleção de ações capazes de selecionar bons conjuntos de ações para agentes complexos situados em ambientes contínuos e dinâmicos. Foram aplicadas com sucesso na Robocup, mas nunca foram aplicadas a jogos. PHISH-Nets, um modelo de redes de comportamentos capaz de selecionar apenas uma ação por vez, foi aplicado à modelagem de personagens, com bons resultados. Apesar de RCEs serem aplicáveis a um conjunto de domínios maior, nunca foram usadas para modelagem de personagens. Apresenta-se como projetar um agente controlado por uma rede de comportamentos para o domínio do Unreal Tournament e como integrar a rede de comportamentos a sensores nebulosos e comportamentos baseados em máquinas de estado-finito aumentadas. Investiga-se a qualidade da seleção de ações e a correção do mecanismo em uma série de experimentos. A performance é medida através da comparação das pontuações de um agente baseado em redes de comportamentos com outros dois agentes. Um dos agentes foi implementado por outro grupo e usava sensores, efetores e comportamentos diferentes. O outro agente era idêntico ao agente baseado em RCEs, exceto pelo mecanismo de controle empregado. A modelagem de personalidade é investigada através do projeto e análise de cinco estereótipos: Samurai, Veterano, Berserker, Novato e Covarde. Apresenta-se três maneiras de construir personalidades e situa-se este trabalho dentro de outras abordagems de projeto de personalidades. Conclui-se que a rede de comportamentos estendida é um bom mecanismo de seleção de ações para o domínio de jogos de computador e um mecanismo interessante para a construção de agentes com personalidades simples. / This work investigates the application of extended behavior networks to the computer game domain. We use as our test bed the game Unreal Tournament. Extended Behavior Networks (EBNs) are a class of action selection architectures capable of selecting a good set of actions for complex agents situated in continuous and dynamic environments. They have been successfully applied to the Robocup, but never before used in computer games. PHISH-Nets, a behavior network model capable of selecting just single actions, was applied to character modeling with promising results. Although extended behavior networks are applicable to a larger domain, they had not been used to character modeling before. We present how to design an agent with extended behavior networks, fuzzy sensors and finite-state machine based behaviors. We investigate the quality of the action selection mechanism and its correctness in a series of experiments. The performance is assessed comparing the scores of an agent using an extended behavior network against a plain reactive agent with identical sensory-motor apparatus and against a totally different agent built around finite-state machines. We investigate how EBNs fare on agent personality modeling via the design and analysis of five stereotypes in Unreal Tournament. We discuss three ways to build character personas and situate our work within other approaches. We conclude that extended behavior networks are a good action selection architecture for the computer game domain and an interesting mechanism to build agents with simple personalities.

Este trabalho investiga a aplicação de rede de comportamentos estendidas ao domínio de jogos de computador. Redes de comportamentos estendidas (RCE) são uma classe de arquiteturas para seleção de ações capazes de selecionar bons conjuntos de ações para agentes complexos situados em ambientes contínuos e dinâmicos. Foram aplicadas com sucesso na Robocup, mas nunca foram aplicadas a jogos. PHISH-Nets, um modelo de redes de comportamentos capaz de selecionar apenas uma ação por vez, foi aplicado à modelagem de personagens, com bons resultados. Apesar de RCEs serem aplicáveis a um conjunto de domínios maior, nunca foram usadas para modelagem de personagens. Apresenta-se como projetar um agente controlado por uma rede de comportamentos para o domínio do Unreal Tournament e como integrar a rede de comportamentos a sensores nebulosos e comportamentos baseados em máquinas de estado-finito aumentadas. Investiga-se a qualidade da seleção de ações e a correção do mecanismo em uma série de experimentos. A performance é medida através da comparação das pontuações de um agente baseado em redes de comportamentos com outros dois agentes. Um dos agentes foi implementado por outro grupo e usava sensores, efetores e comportamentos diferentes. O outro agente era idêntico ao agente baseado em RCEs, exceto pelo mecanismo de controle empregado. A modelagem de personalidade é investigada através do projeto e análise de cinco estereótipos: Samurai, Veterano, Berserker, Novato e Covarde. Apresenta-se três maneiras de construir personalidades e situa-se este trabalho dentro de outras abordagems de projeto de personalidades. Conclui-se que a rede de comportamentos estendida é um bom mecanismo de seleção de ações para o domínio de jogos de computador e um mecanismo interessante para a construção de agentes com personalidades simples. / This work investigates the application of extended behavior networks to the computer game domain. We use as our test bed the game Unreal Tournament. Extended Behavior Networks (EBNs) are a class of action selection architectures capable of selecting a good set of actions for complex agents situated in continuous and dynamic environments. They have been successfully applied to the Robocup, but never before used in computer games. PHISH-Nets, a behavior network model capable of selecting just single actions, was applied to character modeling with promising results. Although extended behavior networks are applicable to a larger domain, they had not been used to character modeling before. We present how to design an agent with extended behavior networks, fuzzy sensors and finite-state machine based behaviors. We investigate the quality of the action selection mechanism and its correctness in a series of experiments. The performance is assessed comparing the scores of an agent using an extended behavior network against a plain reactive agent with identical sensory-motor apparatus and against a totally different agent built around finite-state machines. We investigate how EBNs fare on agent personality modeling via the design and analysis of five stereotypes in Unreal Tournament. We discuss three ways to build character personas and situate our work within other approaches. We conclude that extended behavior networks are a good action selection architecture for the computer game domain and an interesting mechanism to build agents with simple personalities.

Este trabalho investiga a aplicação de rede de comportamentos estendidas ao domínio de jogos de computador. Redes de comportamentos estendidas (RCE) são uma classe de arquiteturas para seleção de ações capazes de selecionar bons conjuntos de ações para agentes complexos situados em ambientes contínuos e dinâmicos. Foram aplicadas com sucesso na Robocup, mas nunca foram aplicadas a jogos. PHISH-Nets, um modelo de redes de comportamentos capaz de selecionar apenas uma ação por vez, foi aplicado à modelagem de personagens, com bons resultados. Apesar de RCEs serem aplicáveis a um conjunto de domínios maior, nunca foram usadas para modelagem de personagens. Apresenta-se como projetar um agente controlado por uma rede de comportamentos para o domínio do Unreal Tournament e como integrar a rede de comportamentos a sensores nebulosos e comportamentos baseados em máquinas de estado-finito aumentadas. Investiga-se a qualidade da seleção de ações e a correção do mecanismo em uma série de experimentos. A performance é medida através da comparação das pontuações de um agente baseado em redes de comportamentos com outros dois agentes. Um dos agentes foi implementado por outro grupo e usava sensores, efetores e comportamentos diferentes. O outro agente era idêntico ao agente baseado em RCEs, exceto pelo mecanismo de controle empregado. A modelagem de personalidade é investigada através do projeto e análise de cinco estereótipos: Samurai, Veterano, Berserker, Novato e Covarde. Apresenta-se três maneiras de construir personalidades e situa-se este trabalho dentro de outras abordagems de projeto de personalidades. Conclui-se que a rede de comportamentos estendida é um bom mecanismo de seleção de ações para o domínio de jogos de computador e um mecanismo interessante para a construção de agentes com personalidades simples. / This work investigates the application of extended behavior networks to the computer game domain. We use as our test bed the game Unreal Tournament. Extended Behavior Networks (EBNs) are a class of action selection architectures capable of selecting a good set of actions for complex agents situated in continuous and dynamic environments. They have been successfully applied to the Robocup, but never before used in computer games. PHISH-Nets, a behavior network model capable of selecting just single actions, was applied to character modeling with promising results. Although extended behavior networks are applicable to a larger domain, they had not been used to character modeling before. We present how to design an agent with extended behavior networks, fuzzy sensors and finite-state machine based behaviors. We investigate the quality of the action selection mechanism and its correctness in a series of experiments. The performance is assessed comparing the scores of an agent using an extended behavior network against a plain reactive agent with identical sensory-motor apparatus and against a totally different agent built around finite-state machines. We investigate how EBNs fare on agent personality modeling via the design and analysis of five stereotypes in Unreal Tournament. We discuss three ways to build character personas and situate our work within other approaches. We conclude that extended behavior networks are a good action selection architecture for the computer game domain and an interesting mechanism to build agents with simple personalities.

For a long time, the function of the musical score has been to support activity in video games, largely by reinforcing the drama and excitement. Rather than leave the score in the background, this project explores the interaction possibilities of an adaptive video game score using real-time modulation of granular synthesis. This study evaluates a vertically re-orchestrated musical score with elements of the score being played back with granular synthesis. A game level was created where parts of the musical score utilized one granular synthesis stem, the parameters of which were controlled by the player. A user experience study was conducted to evaluate the granular synthesis interaction. The results show a wide array of user responses, opinions, impression and recommendations about how the granular synthesis interaction was musically experienced. Some results show that the granular synthesis stem is regarded as an interactive feature and have a direct relationship to the background music. Other results show that interaction went unnoticed. In most cases, the granular synthesis score was experienced as comparable to a more conventional game score and so, granular synthesis can be seen a new interactive tool for the sounddesigner. The study shows that there is more to be explored regarding musical interactions within games. / <p>For contact with the author or request of videoclips, audio or other resources</p><p>Mail: olliver.andersson@gmail.com</p>

This thesis explores the perceived enjoyability of Deep Reinforcement learning AI agents (DeepRL agent) that strives towards optimality within the First Person Shooter game Unreal Tournament 2004 (UT2004). The DeepRL agent used in the experiments was created and then trained within this game against the AI agent which comes with the UT2004 game (known here as a trivial UT2004 agent). Through testing the opinions of participants who have played UT2004 deathmatches against both the DeepRL agent and the trivial UT2004 agent, the data collected in two participant surveys shows that the DeepRL agent is more enjoyable to face than a trivial UT2004 agent. By striving towards optimality the DeepRL agent developed a behaviour which despite making the DeepRL agent a great deal worse at UT2004 than the trivial UT2004 agent was more enjoyable to face than the trivial UT2004 agent. Considering this outcome the data suggests that DeepRL agents in UT2004 which are encouraged to strive towards optimality during training are “enjoyable enough” in order to be considered by game developers to be “good enough” when developing non-trivial opponents for games similar to UT2004. If the development time of a DeepRL agent is reduced or equal in comparison with the development time of a trivial agent then the DeepRL agent could hypothetically be preferable.