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• W2230033167 abstract "Real-time strategy (RTS) games are hard from an AI point of view because they have enormous state spaces, combinatorial branching factors, allow simultaneous and durative actions, and players have very little time to choose actions. For these reasons, standard game tree search methods such as alphabeta search or Monte Carlo Tree Search (MCTS) are not sufficient by themselves to handle these games. This paper presents an alternative approach called Adversarial Hierarchical Task Network (AHTN) planning that combines ideas from game tree search with HTN planning. We present the basic algorithm, relate it to existing adversarial hierarchical planning methods, and present new extensions for simultaneous and durative actions to handle RTS games. We also present empirical results for the µRTS game, comparing it to other state of the art search algorithms for RTS games." @default.
• W2230033167 created "2016-06-24" @default.
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• W2230033167 date "2015-07-25" @default.
• W2230033167 modified "2023-10-02" @default.
• W2230033167 title "Adversarial hierarchical-task network planning for complex real-time games" @default.
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