Mixed Reality Meets Procedural Content Generation in Video Games

Azad, Sasha; Saldanha, Carl; Gan, Cheng-Hann; Riedl, Mark O.

doi:10.1609/aiide.v12i2.12898

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…They have also highlighted the fact that the awareness of using PCG for virtual worlds has increased significantly in recent years, not only through researchers, but mainly through practitioners. One such work, [14] has made use of the surrounding of the user's physical space to create the game path and obstacles in mixed reality.…”

Section: B Procedural Content Generation (Pcg)mentioning

confidence: 99%

Procedural Content Generation Using Reinforcement Learning for Disaster Evacuation Training in a Virtual 3D Environment

Agarwal,

Shridevi

2023

IEEE Access

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This research addresses the need for effective disaster evacuation training methods by proposing a virtual reality system that utilizes Reinforcement Learning Procedural Content Generation (RL-PCG) algorithms. The aim of this study is to provide a cost-effective and safe way to conduct disaster evacuation preparedness training, surpassing the limitations of traditional real-life drills. The paper's objectives encompass the design of a novel 3-layer PCG architecture for generating realistic disaster simulations in virtual reality, the implementation of a working prototype for fire disaster scenarios, and the evaluation of the proposed system's effectiveness through comparison with existing RL agents. Significant findings include the superiority of the RL-PCG agent in generating diverse and realistic disaster scenarios with faster training time and lesser number of steps, even with limited processor capabilities. In conclusion, this research establishes that the RL-PCG Scenario for Disaster Evacuation Training in VR is a more effective method, leading to improved disaster preparedness for individuals, and opens avenues for further advancements in disaster training using virtual reality and reinforcement learning technologies. For a video demo of this work, please visit https://youtu.be/3WZnQOfUP94.INDEX TERMS Disaster evacuation training, procedural content generation, reinforcement learning, virtual reality.

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Section: B Procedural Content Generation (Pcg)mentioning

confidence: 99%

Procedural Content Generation Using Reinforcement Learning for Disaster Evacuation Training in a Virtual 3D Environment

Agarwal,

Shridevi

2023

IEEE Access

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“…Researchers have manually authored narratives to document cultural heritage and community-based narratives or goals (Speiginer et al 2015) as well as procedurally generated games and narratives for various geo-locations populated with NPCs (Macvean et al 2011;Dow et al 2006;Leino, Wirman, and Fernandez 2008;Azad et al 2016). We posit that NPCs in real-world locations must be able to learn cultural, and societal values of the location they populate.…”

Section: Believable Non-player Characters (Npcs)mentioning

confidence: 99%

Lyra: Simulating Believable Opinionated Virtual Characters

Azad

Martens

2019

AIIDE

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Creating believable simulations of large populations of characters in virtual worlds represents a grand challenge for interactive artificial intelligence, requiring reasoning about social intelligence. In this paper, we focus on one aspect of this challenge: the dynamics of opinion change for virtual characters and its relationship with social affinity. We developed a simulated population of characters that debate politically-charged topics, called Lyra. Characters’ knowledge, opinions, and biases spread through this society based on existing cognitive models and social science theories. Our simulation generates outlines of group conversations that portray the system’s evolution, and clusters characters into affinity groups based on the outcome of the debates. We conducted a human-subjects study to evaluate these generated conversations and affinity groups for their believability and to inform future iterations of the simulation. We believe successful simulation of opinion change in social dynamics provides a foundation for computational recognition, prediction, and interfacing with humans.

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