The application of artificial intelligence (AI) to real-time strategy (RTS) games includes considerable challenges due to the very large state spaces and branching factors, limited decision times, and dynamic adversarial environments involved. To address these challenges, hierarchical task network (HTN) planning has been extended to develop a method denoted as adversarial HTN (AHTN), and this method has achieved favorable performance. However, the HTN description employed cannot express complex relationships among tasks and accommodate the impacts of the environment on tasks. Moreover, AHTN cannot address task failures during plan execution. Therefore, this paper proposes a modified AHTN planning algorithm with failed task repair functionality denoted as AHTN-R. The algorithm extends the HTN description by introducing three additional elements: essential task , phase , and exit condition . If any task fails during plan execution, the AHTN-R algorithm identifies and terminates all affected tasks according to the extended HTN description, and applies a novel task repair strategy based on a prioritized listing of alternative plans to maintain the validity of the previous plan. In the planning process, AHTN-R generates the priorities of alternative plans by sorting all nodes of the game search tree according to their primary features. Finally, empirical results are presented based on a μRTS game, and the performance of AHTN-R is compared to that of AHTN and to the performances of other state-of-the-art search algorithms developed for RTS games.
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