Heuristic Search for Physics-Based Problems: Angry Birds in PDDL+
Keywords:Mixed discrete/continuous planning
AbstractThis 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
Industry and Applications Track