Game design,
programming,
deep learning
Welcome to my portfolio page! My name is Javier Roldรกn Botรญas. I am a graduated game designer and programmer with a Master’s degree in deep learning. I love playing and creating videogames.
Individual work
How videogames can be used to intensify horror
experiences via universalization and personalization
Overview
Objectives
Specs
Results
Horror and Personalization
Have you ever gone out to see a movie with friends? Have they cried and shrieked as you remained unfazed, or vice-versa? This happened to me once. When I was little, my father swore that The Exorcist was one of the scariest movies ever made. Watched it, nothing. Keep in mind that this was the same me that couldnยดt sleep for weeks after watching Gremlins, so why does this happen? ๐๐ก๐ฒ ๐ข๐ฌ ๐ก๐จ๐ซ๐ซ๐จ๐ซ ๐ฌ๐๐๐ซ๐ฒ ๐๐๐๐๐๐๐๐๐? ๐๐๐ง ๐ ๐ก๐จ๐ซ๐ซ๐จ๐ซ ๐ญ๐ก๐๐ญ ๐ฌ๐๐๐ซ๐๐ฌ ๐๐ฏ๐๐ซ๐ฒ๐๐จ๐๐ฒ ๐๐ฑ๐ข๐ฌ๐ญ?
For my bachelorโs degree project, I wrote the paper: โ๐๐ฐ๐ธ ๐ท๐ช๐ฅ๐ฆ๐ฐ๐จ๐ข๐ฎ๐ฆ๐ด ๐ค๐ข๐ฏ ๐ฃ๐ฆ ๐ถ๐ด๐ฆ๐ฅ ๐ต๐ฐ ๐ช๐ฏ๐ต๐ฆ๐ฏ๐ด๐ช๐ง๐บ ๐ฉ๐ฐ๐ณ๐ณ๐ฐ๐ณ ๐ฆ๐น๐ฑ๐ฆ๐ณ๐ช๐ฆ๐ฏ๐ค๐ฆ๐ด ๐ท๐ช๐ข ๐ถ๐ฏ๐ช๐ท๐ฆ๐ณ๐ด๐ข๐ญ๐ช๐ป๐ข๐ต๐ช๐ฐ๐ฏ ๐ข๐ฏ๐ฅ ๐ฑ๐ฆ๐ณ๐ด๐ฐ๐ฏ๐ข๐ญ๐ช๐ป๐ข๐ต๐ช๐ฐ๐ฏโ. The main objective of the work was to determine the plausibility of the aforementioned horror that scares everybody. For this, I divided the main horror genres into their main aspects. For example, blood and viscera for gore, unexplainable fear for cosmic horror and diokophobia (fear of being chased) for slashers. The effects caused on people were then measured using non-invasive methods. The experimentation was used to determine if what scares someone could be predicted. A score was calculated to anticipate which of six selected games would cause the most intense reaction.
Ultimately, the model for videogame use proved to be possible, as the fear response can be accurately measured with a simple survey that could be found at any point of an experience. Universal horrors could be even more specific with the many users that come with a videogame product. The data could be used similarly to the personalization model, with general templates that provide the groundwork for further modifications. The horror categories that create more enjoyable experiences and those external to the horror genre proved to be more difficult to measure, although almost no individual enjoyed the one that caused the highest distress the most.
The rig used for the measurements included a 4 channel electroencephalogram (EEG-SMT), a FitBit watch for heart rate monitoring and a camera for blinks. The software used was OpenVibe, detecting beta and delta waves.
For the experimentation, I required a non-expensive method of identifying the fear response, as it could be used in future lines of investigation. The EEG-SMT and the inexpensive setup with household items were perfect for the testing, as they use already established technology to push the boundaries of videogame personalization. The survey was essential to see how generalized horror worked and revealed fears that hadnโt even registered for me at first, like insects and clowns.
How videogames can be used to intensify horror
experiences via universalization and personalization
Overview
Problems
Final model
Conclusion
Videogame testing automatization
Video game testing is a bottleneck. If you are a developer, youโve probably had to wait for new mechanics, geometry, code and AI (NPCs) to be tested. Spending hours on a single bug is not an uncommon experience. This tedious work can be sped up significantly by using a ๐๐ฆ๐ฆ๐ฑ ๐๐ฆ๐ช๐ฏ๐ง๐ฐ๐ณ๐ค๐ฆ๐ฎ๐ฆ๐ฏ๐ต ๐๐ฆ๐ข๐ณ๐ฏ๐ช๐ฏ๐จ ๐ข๐จ๐ฆ๐ฏ๐ต, which can interact with your specific environment to find bugs. This is exactly what I developed in my paper: ๐๐ถ๐ต๐ฐ๐ฎ๐ข๐ต๐ช๐ค ๐๐ฆ๐ต๐ฆ๐ค๐ต๐ช๐ฐ๐ฏ ๐ฐ๐ง ๐๐ถ๐จ๐ด ๐ช๐ฏ ๐๐ช๐ฅ๐ฆ๐ฐ๐จ๐ข๐ฎ๐ฆ๐ด ๐ฃ๐บ ๐๐ฆ๐ช๐ฏ๐ง๐ฐ๐ณ๐ค๐ฆ๐ฎ๐ฆ๐ฏ๐ต ๐๐ฆ๐ข๐ณ๐ฏ๐ช๐ฏ๐จ ๐ธ๐ช๐ต๐ฉ ๐๐ฏ๐ต๐ณ๐ช๐ฏ๐ด๐ช๐ค ๐๐ถ๐ณ๐ช๐ฐ๐ด๐ช๐ต๐บ ๐ข๐ฏ๐ฅ ๐๐ฆ๐ฎ๐ฑ๐ฐ๐ณ๐ข๐ญ ๐๐ฏ๐ข๐ญ๐บ๐ด๐ช๐ด ๐ธ๐ช๐ต๐ฉ ๐๐๐๐.
The unique training environment makes reinforcement learning possible, in which instead of finding a structured function in a dataset, the objective is to optimize an extrinsic reward function crafted by the developer. This approach is much more similar to how human learning works, as the agent has to interact with the environment. Intrinsic curiosity works in parallel with the extrinsic reward function, as it rewards the agent for taking actions that are innovative, making it easier to generalize and less prone to โreward hackingโ.
The final agent is an LSTM neural network, making it very lightweight. It was tested on three different maps with similar mechanics, and a final map in a different project with different mechanics and movement values. The input received by the agent was a normalized array of raycasts to determine the distance to each point in the map.
The agent data (position, distance to each raycast) is passed through another LSTM to find the bugs in the temporal context. These bugs are registered to a coordinate (x,y,z) inside the environment to flag the error. As it is ๐ต๐ฆ๐ฎ๐ฑ๐ฐ๐ณ๐ข๐ญ data, the bugs that can be found are restricted to ๐ต๐ฆ๐ฎ๐ฑ๐ฐ๐ณ๐ข๐ญ errors, which include: map bounds, geometry errors and value errors (like velocity).
Many different maps were used for the testing and training, including 3d maps with no verticality, some with verticality and an external project to test and train the final model, with more complex inputs, different movement values and scale. The different models and hyperparameters were tested on the maps to determine the most effective ones.
Videogame errors are very varied and can exist beyond temporal values, like textures and visual bugs, and parameter errors. The experimentation proved effective on temporal bugs, which were the main focus and can be easily detectable with an LSTM. Many other methods can be used to find errors, but this one is lightweight and effective, making for easy testing and shorter sessions.
Group work
Skate Summit
A platforming skate game with a nostalgic touch. A game packed with skate mechanics, without the overwhelming simulator commands, set in a cyberpunk world with retro vibes. Jump, grind, hit half-pipes, or go wild doing tricks!
Responsibilities:
- UI/UX Design and implementation
- Mission design and implementation
- Narrative designer
- General programming
