A Multiagent Potential Field‐Based Bot for Real‐Time Strategy Games

Abstract

Bots for real-time strategy (RTS) games may be very challenging to implement. A bot controls a number of units that will have to navigate in a partially unknown environment, while at the same time avoid each other, search for enemies, and coordinate attacks to fight them down. Potential fields are a technique originating from the area of robotics where it is used in controlling the navigation of robots in dynamic environments. Although attempts have been made to transfer the technology to the gaming sector, assumed problems with efficiency and high costs for implementation have made the industry reluctant to adopt it. We present a multiagent potential field-based bot architecture that is evaluated in two different real-time strategy game settings and compare them, both in terms of performance, and in terms of softer attributes such as configurability with other state-of-the-art solutions. We show that the solution is a highly configurable bot that can match the performance standards of traditional RTS bots. Furthermore, we show that our approach deals with Fog of War (imperfect information about the opponent units) surprisingly well. We also show that a multiagent potential field-based bot is highly competitive in a resource gathering scenario.

A Survey of Real-Time Strategy Game AI Research and Competition in StarCraft

“…Some uses of potential fields in RTS games are: avoiding obstacles (navigation), avoiding opponent fire [41], or staying at maximum shooting distance [42]. Potential fields have also been combined with A* path-finding to avoid local traps [43].…”

Towards the design of a human-like FPS NPC using pheromone maps

“…This concept has been applied to games as well, with some authors arguing that a potential field approach can be a viable and cheaper option to replace more conventional, A * path finding [7]. In [8], an algorithm is proposed that determines where is the best place for Pac-Man to move, based on attractiveness of potential fields, taking into consideration the moving ghosts and dots to eat.…”

A study on human like characteristics in real time strategy games

“…The bots were implemented using the multi-agent potential field approach described by Hagelbäck and Johansson in [7], [8], [9]. Two different bots were created, called Largebase and Tankrush.…”