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The HTN Planning plugin lets you create AI that can plan multiple steps ahead by predicting the consequences of its actions. Here are some examples:
Video: an HTN-based character inventing the optimal plan to attack a target
Video: two groups of HTN-based characters tactically fighting in a simple FPS arena
Just like with Behavior Trees, you can easily create Tasks, Decorators, and Services from either C++ or Blueprints, and arrange them in a visual graph editor. The HTN planner uses Blackboard data to store knowledge about possible future worldstates. Nodes check and modify values in the worldstate during planning, which makes it possible to make decisions based on possible future states. The planner efficiently finds the plan with the lowest cost, or the one with the highest priority.
Compared to other planning techniques like Goal-Oriented Action Planning, HTN planning is more efficient and gives designers much more control over the AI. It can be as rigid as Behavior Trees, or as flexible as GOAP. You can create AI with just as much autonomy and flexibility as you need: an AI that selects between predefined sequences of tasks, or an AI that is free to arrange its tasks in any order to achieve a goal, or anything in between.
HTN Planning 插件可让您创建 AI，通过预测其操作的后果来提前规划多个步骤。 这里有些例子：
视频：基于 HTN 的角色发明了攻击目标的最佳计划
视频：两组基于 HTN 的角色在简单的 FPS 竞技场中进行战术战斗
就像行为树一样，您可以轻松地从 C++ 或蓝图创建任务、装饰器和服务，并将它们排列在可视化图形编辑器中。 HTN 规划器使用 Blackboard 数据来存储有关未来可能的世界状态的知识。 节点在规划期间检查和修改世界状态中的值，这使得可以根据未来可能的状态做出决策。 规划者可以有效地找到成本最低的计划或优先级最高的计划。
与目标导向行动规划等其他规划技术相比，HTN 规划更加高效，并且使设计人员能够更好地控制人工智能。 它可以像行为树一样严格，也可以像 GOAP 一样灵活。 您可以根据需要创建具有尽可能多的自主性和灵活性的人工智能：可以在预定义的任务序列之间进行选择的人工智能，或者可以以任何顺序自由安排其任务以实现目标的人工智能，或者介于两者之间的任何人工智能。
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