Heuristic Search for Physics-Based Problems: Angry Birds in PDDL+


  • Wiktor Piotrowski Parc
  • Yoni Sher Parc
  • Sachin Grover Parc
  • Roni Stern Ben Gurion University
  • Shiwali Mohan Parc




Mixed discrete/continuous planning


This paper studies how a domain-independent planner and combinatorial search can be employed to play AngryBirds, 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 PDDL+ 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.




How to Cite

Piotrowski, W., Sher, Y., Grover, S., Stern, R., & Mohan, S. (2023). Heuristic Search for Physics-Based Problems: Angry Birds in PDDL+. Proceedings of the International Conference on Automated Planning and Scheduling, 33(1), 518-526. https://doi.org/10.1609/icaps.v33i1.27232