Heuristic Search For Physics-Based Problems: Angry Birds in PDDL+
This addresses the challenge of applying general AI planning to a complex physics-based game, but it is incremental as it builds on existing PDDL+ methods and domain-specific solvers.
The paper tackles the problem of playing Angry Birds using domain-independent planning and combinatorial search, modeling it with PDDL+ and proposing domain-specific enhancements like heuristics and search techniques. The results show performance on par with dedicated domain-specific solvers in most levels, even without enhancements.
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.