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

TL;DR

This paper demonstrates how a domain-independent planner using PDDL+ can effectively play Angry Birds by modeling the game as a mixed discrete/continuous problem, employing heuristics and search enhancements to improve performance.

Contribution

It introduces a novel application of PDDL+ for modeling Angry Birds and proposes domain-specific heuristics and search techniques to manage complexity.

Findings

Performance comparable to domain-specific solvers on many levels

Heuristics and search enhancements improve efficiency

Modeling with PDDL+ is effective for mixed discrete/continuous problems

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.