A Comprehensive Categorization Framework for Interactive Fiction Games

Abstract

Interactive Fiction (IF) games are digital experiences that merge storytelling with interactive gameplay, allowing players to navigate and influence story-driven adventures. These games have evolved significantly, integrating advanced visual and interactive elements alongside traditional textual narratives, making them an intriguing area of study. However, currently, there are few structured frameworks designed for the systemic classification of IF games and it can be challenging to analyze these games wholistically.This thesis presents a comprehensive categorization framework for IF games, designed to facilitate systematic classification and analysis. Based on features derived from common video game features, including human-computer interface, game genres, game mechanics, and business model, the framework supports the classification of IF games into distinct categories. This structured approach allows feature-based examination and facilitates the holistic analysis of IF games and their evolution. Validation for the proposed framework involved three rounds of sampling and categorizing IF games. The first round sampled popular IF games developed based on well-established game engines to demonstrate the fundamental validity of the framework. The second round sampled popular IF games over time for insights into potential trends as IF games continue to develop and evolve. The third round was based on popular IF games developed by the same studio to examine the potential trend of IF games after removing the bias of developers. The three rounds of sampling and categorizing reveal potential patterns and trends that enhance the understanding of IF games. Key insights include the trends from text-only to image-based or even animation-based output, from no or little towards more sophisticated support for stats and resource management, and the potential overlapping and merging of IF and action-adventure games. These insights can serve as references for future IF game development. These findings demonstrate that the proposed framework is an effective tool for systematic analysis that can offer valuable insights into the development and trends of IF games. Since classification involves subjectivity, future work should repeat the process based on stakeholders with distinct backgrounds, e.g., publishers, developers, and gamers. Additionally, the proposed framework is but a first step and should be continuously reviewed and refined.