Heuristic Search For Physics-Based Problems: Angry Birds in PDDL+
Abstract
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.
Keywords
Cite
@article{arxiv.2303.16967,
title = {Heuristic Search For Physics-Based Problems: Angry Birds in PDDL+},
author = {Wiktor Piotrowski and Yoni Sher and Sachin Grover and Roni Stern and Shiwali Mohan},
journal= {arXiv preprint arXiv:2303.16967},
year = {2023}
}