AAAI Publications, Workshops at the Twenty-Fourth AAAI Conference on Artificial Intelligence

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Opponent Behaviour Recognition for Real-Time Strategy Games
Froduald Kabanza, Philipe Bellefeuille, Francis Bisson, Abder Rezak Benaskeur, Hengameh Irandoust

Last modified: 2010-07-07


In Real-Time Strategy (RTS) video games, players (controlled by humans or computers) build structures and recruit armies, fight for space and resources in order to control strategic points, destroy the opposing force and ultimately win the game. Players need to predict where and how the opponents will strike in order to best defend themselves. Conversely, assessing how the opponents will defend themselves is crucial to mounting a successful attack while exploiting the vulnerabilities in the opponent's defence strategy. In this context, to be truly adaptable, computer-controlled players need to recognize their opponents' behaviour, their goals, and their plans to achieve those goals. In this paper we analyze the algorithmic challenges behind behaviour recognition in RTS games and discuss a generic RTS behaviour recognition system that we are developing to address those challenges. The application domain is that of RTS games, but many of the key points we discuss also apply to other video game genres such as multiplayer first person shooter (FPS) games.


plan recognition; intent recognition; goal recognition; threat evaluation; opponent modelling; HICOR

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