Tension Space Analysis for Emergent Narrative

Emergent narratives provide a unique and compelling approach to interactive storytelling through simulation, and have applications in games, narrative generation, and virtual agents. However the inherent complexity of simulation makes understanding the expressive potential of emergent narratives difficult, particularly at the design phase of development. In this paper, we present a novel approach to emergent narrative using the narratological theory of possible worlds and demonstrate how the design of works in such a system can be understood through a formal means of analysis inspired by expressive range analysis. Lastly, we propose a novel way through which content may be authored for the emergent narrative system using a sketch-based interface.

Offline Grid-Based Coverage path planning for guards in games

Algorithmic approaches to exhaustive coverage have application in video games, enabling automatic game level exploration. Current designs use simple heuristics that frequently result in poor performance or exhibit unnatural behaviour. In this paper, we introduce a novel algorithm for covering a 2D polygonal (with holes) area. We assume prior knowledge of the map layout and use a grid-based world representation. Experimental analysis over several scenarios ranging from simple layouts to more complex maps used in actual games show good performance. This work serves as an initial step towards building a more efficient coverage path planning algorithm for non-player characters.

Generating Paths with WFC

Motion plans are often randomly generated for minor game NPCs. Repetitive or regular movements, however, require non-trivial programming effort and/or integration with a pathing system. We here describe an example-based approach to path generation that requires little or no additional programming effort. Our work modifies the Wave Function Collapse (WFC) algorithm, adapting it to produce pathing plans similar to an input sketch. We show how simple sketch modifications control path characteristics, and demonstrate feasibility through a usable Unity implementation.

Exploration in NetHack With Secret Discovery

Roguelike games generally feature exploration problems as a critical, yet often repetitive element of gameplay. Automated approaches, however, face challenges in terms of optimality, as well as due to incomplete information, such as from the presence of secret doors. This paper presents an algorithmic approach to exploration of roguelike dungeon environments. Our design aims to minimize exploration time, balancing coverage and discovery of secret areas with resource cost. Our algorithm is based on the concept of occupancy maps popular in robotics, adapted to encourage efficient discovery of secret access points. Through extensive experimentation on NetHack maps we show that this technique is significantly more efficient than simpler greedy approaches and an existing automated player. We further investigate optimized parameterization for the algorithm through a comprehensive data analysis. These results point towards better automation for players as well as heuristics applicable to fully automated gameplay.