A Multi-Agent Potential Field based approach for Real-Time Strategy Game bots
Abstract: Computer games in general and Real-Time Strategy (RTS) games in particular provide a rich challenge for both human- and computer controlled players, often denoted as bots. The player or bot controls a large number of units that have to navigate in partially unknown dynamic worlds to pursue a goal. Navigation in such worlds can be complex and require much computational resources. Typically it is solved by using some sort of path planning algorithm, and a lot of research has been conducted to improve the performance of such algorithms in dynamic worlds. The main goal of this thesis is to investigate an alternative approach for RTS bots based on Artificial Potential Fields, an area originating from robotics. In robotics the technique has successfully been used for navigation in dynamic environments, and we show that it is possible to use Artificial Potential Fields for navigation in an RTS game setting without any need of path planning. In the first three papers we define and demonstrate a methodology for creating multi-agent potential field based bots for an RTS game scenario where two tank armies battle each other. The fourth paper addresses incomplete information about the game world, referred to as the fog of war, and show how Potential Field based bots can handle such environments. The final paper shows how a Potential Field based bot can be evolved to handle a more complex full RTS scenario. It addresses resource gathering, construction of bases, technological development and construction of an army consisting of different types of units. We show that Artificial Potential Fields is a viable option for several RTS game scenarios and that the performance, both in terms of being able to win a game and computational resources used, can match and even surpass those of traditional approaches based on path planning.
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