Together with the growth of procedural content generation in game development, there is a need for a viable generation method of procedural context to make sense of the content within game space. Previous research discusses how interactivity and narrative are almost opposite of each other and when combined needs to be generated in two steps, one for the game space and the other for context. We propose procedural narrative as context through objectives, as a useful means to structure content in games. In this paper we present and describe an artefact developed as a sub-system to the Evolutionary Dungeon Designer (EDD), that procedurally generates objectives for the dungeons created with the tool. The artefact is developed with macro patterns which can be defined as an extension of the existing meso patterns in EDD. The macro patterns are used to generate objectives in the rooms of the dungeons, and the system evaluates the priority of the room objectives based on the design of the dungeon and the quality of the objective to maximize the usage of game space and create a suitable narrative. The work for this thesis and its artefact resulted in a successful expansion on the knowledge of procedural narrative generation by presenting macro patterns as a viable solution for contextualization of procedural game content.

A large part of any game development process consists of content creation, which costs both time and effort. Procedural generation techniques exist to help narrative generation, but they are scattered and require extensive manual labour to set up. On top of that, Serious Games content created with these techniques tend to be uninteresting and lack variety which can ultimately lead to the Serious Games missing their intended purpose. This paper delivers a prototype for a modular tool that aims to solve these problems by combining existing narrative generation techniques with common sense database knowledge and player adaptivity techniques. The prototype tool implements Ceptre as a core module for the generation of stories and ConceptNet as a commonsense knowledge database. Two studies have been conducted with content created by the tool. One study tested if generation rules created by commonsense can be used to flesh out stories, while the other one evaluated if adapted stories yield better scores. The results of the first test state that adding rules retrieved through common sense knowledge did not improve story quality, but they can however be used to extend stories without compromising story quality. It also shows that ideally, an extensive natural language processing module should be used to present the stories rather than a basic implementation. The statistically insignificant result of the second test was potentially caused by the compromises taken when conducting the test. Reconduction of this test using real game data, rather than data from the compromised personality test, might be preferable.

Procedural content generation is a technique used to produce a wide range of computer-generated content in many industries today, the video game industry in particular. This study focuses on how procedural content generation can be applied to create emotionally interesting non-player characters and through this, generate narrative snippets that can immerse and interest a reader. The main points examined are how to achieve this using a modular approach to personality and behaviour, how well readers can distinguish whether motivations and interactions are generated by a computer or written by a human, and to what degree a reader can be immersed in a computer-generated narrative. Procedural narrative could help to reduce workload on large projects or lower costs, and is an area in which there is much room for further research. To answer these problems, a literature review of existing techniques for the creation of emotionally interesting non-player characters was conducted and used to design and construct a prototype implementation for generating procedural narrative. The output of this narrative was dressed up to match the style of a human text and A/B testing was conducted utilising a survey in order to evaluate and compare responses to the two texts. Ultimately, the results showed very little difference between the perception of the human-written text and the computer-written text, with the only aspects found lacking in the computer-written text being clarity of emotion and foreshadowing.