DOAP

Premise

Difficulty Oriented Action Planning (DOAP) is a framework that implements Dynamic Difficulty Adjustment (DDA) in Goal Oriented Action Planning (GOAP) agents, altering their level of artificial stupidy based on player performance.
To test this framework, we built a stealth game where the player takes the role of the real-life art-thief Vjeran Tomic, during his 2010 burglary of the Museum of Modern Art in Paris. The game has two DOAP controlled guards roam the museum, while the player tries to aboid them and steal five paintings.

Project Work

The project was created in Unity in a team of five people. I programmed the DDA implementation, creating a solution that allowed other developers to easily alter to what degree player actions affected the difficulty, and how the difficulty affected GOAP weights and other GOAP Agent aspects without requiring them to write any code.
Another group member and I coordinated the user tests, with me handling all of the data analysis after the tests' conclusion. I also created a screen-based outline shader to give the game a more appealing art style, and designed some of the UI elements. Finally, I made both the cutscenes of the project and its associated AV Production, working with both internal and external actors.