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.

本文研究了如何使用领域无关的规划器和组合搜索来玩《愤怒的小鸟》，使用了 PDDL + 进行了建模，并提出了多个领域特定的增强措施，包括启发式和类似首选操作员的搜索技术，研究对比表明我们的方法在大多数水平上与定制的特定领域解算器的表现相当，即使不使用领域特定的搜索增强措施。

基于启发式搜索的物理问题求解方法：PDDL+中的愤怒的小鸟