Procedural content generation in gaming via evolutionary algorithms

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThe aim of this thesis is to investigate the possibility of creating content using the Genetic Algorithms. To this end a simple system of interconnected algorithms were developed using concepts from Role Playing Games, specifically Dungeons and Dragons to create game content as characters, quests, and encounters. To be able to produce context, subsystems of map, character, quest, and encounter generators were created. These systems or engines not only define the game space to be populated, but they also provide each other input to create maps, quests, locations, animals, and events that are sensible and coherent. Randomness of the generation was essential as such a variety of noise maps and random number generation were added to every engine in the system. Layered or singular noise maps allowed for logical assumptions to be made, like seeing camels in a location with no rain and high temperatures. With the base truth coming from a random noise map such as danger, civilisation, faction etc., each system built on top of each other can get more complex. There are several Genetic Algorithms with custom operators within the system. These algorithms take their inputs and individuals from the respective engines and tie them all to each other through their physical coordinates in the gaming space. The most impactful part of these algorithms is the Fitness Functions defined with concepts from literature or CGI. The proposed system can populate a game space with elements of desired attributes given the constraints. The output produced consists of coherently tied story beats with some attributes already set. Even in this simple level, this can allow not only game designers but anyone who wants to build any kind of fictional work