Intelligent group movement and selection in realtime strategy games
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Date
2008-12
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Abstract
Movement of groups in realtime strategy games is often a nuisance: Units travel and battle separately, resulting in huge losses and the AI looking dumb. This applies to computer as well as human commanded factions. We suggest to tackle that by using flocking improved by influence-map based pathfinding which leads to a much more natural and intelligent looking behavior. A similar problem occurs if the computer AI has to select groups to combat a specific target: Assignment of units to groups, especially for multiple enemy groups, is often suboptimal when units have very different attack skills. This can be cured by using offline prepared self-organizing feature maps that use all available information for looking up good matches. We demonstrate that these two approaches work well separately, but also that they go together very naturally, thereby leading to an improved and - via parametrization - very flexible group behavior. Opponent AI may be strenghtened that way as well as player-supportive AI. A thorough experimental analysis supports our claims.
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Keywords
evolutionary algorithms, neural networks, path finding, realtime strategy games, tactical decision making