Enhancing Game Agent Pathfinding Through Dynamic Graph Reweighting

Abstract

This paper proposes a computationally inexpensive algorithm that utilizes player data to optimize nonplayer character pathfinding in a competitive, multiplayer environment and focuses on imitating the player behavior. The algorithm’s input consists of player statistics gathered during the current and previous matches with additional time and space context, similar in design to influence maps. The input is then enriched with two additional, novel variables, allowing easy online fine-tuning of the output. The obtained result influences the final edge values of the map graph. Any known pathfinding algorithm that works with digraphs can then be utilized to control the agent. This paper contains exemplary results obtained when analyzing input on a map modeled after an existing map in the video game Unreal Tournament.