Heuristic Search For Physics-Based Problems: Angry Birds in PDDL+
Wiktor Piotrowski, Yoni Sher, Sachin Grover, Roni Stern, Shiwali Mohan

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.
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Taxonomy
TopicsArtificial Intelligence in Games · AI-based Problem Solving and Planning · Digital Games and Media
