This paper studies how a domain-independent planner and combinatorial search can be employed to play Angry Birds, a well established AI challenge problem. To model the game, we use PDDL+, a planning language for mixed discrete/continuous domains that supports durative processes and exogenous events. The paper describes the model and identifies key design decisions that reduce the problem complexity. In addition, we propose several domain-specific enhancements including heuristics and a search technique similar to preferred operators. Together, they alleviate the complexity of combinatorial search. We evaluate our approach by comparing its performance with dedicated domain-specific solvers on a range of Angry Birds levels. The results show that our performance is on par with these domain-specific approaches in most levels, even without using our domain-specific search enhancements.

翻译：本文研究了如何将领域无关的规划器与组合搜索应用于《愤怒的小鸟》这一公认的人工智能挑战问题。为了对游戏进行建模，我们采用了PDDL+——一种支持持续过程和外部事件的混合离散/连续领域的规划语言。本文描述了该模型，并确定了降低问题复杂度的关键设计决策。此外，我们提出了若干领域特定的增强方法，包括启发式算法和类似于偏好算子的搜索技术。这些方法共同缓解了组合搜索的复杂度。我们通过在一系列《愤怒的小鸟》关卡中将我们的方法与专用领域求解器进行性能对比来评估该方法。结果表明，即使未使用领域特定的搜索增强方法，我们的性能在大多数关卡中与这些专用领域方法相当。