Playing Angry Birds with a Domain-Independent PDDL+ Planner
This demonstrates the applicability of domain-independent planning to the Angry Birds AI challenge, though it appears incremental as it adapts existing planning methods to a new domain.
The authors tackled the problem of playing Angry Birds using a domain-independent planner by modeling the game with PDDL+ and generating plans with a domain-independent PDDL+ planner. The result showed that their system's performance was on par with domain-specific systems for this AI benchmark.
This demo paper presents the first system for playing the popular Angry Birds game using a domain-independent planner. Our system models Angry Birds levels using PDDL+, a planning language for mixed discrete/continuous domains. It uses a domain-independent PDDL+ planner to generate plans and executes them. In this demo paper, we present the system's PDDL+ model for this domain, identify key design decisions that reduce the problem complexity, and compare the performance of our system to model-specific methods for this domain. The results show that our system's performance is on par with other domain-specific systems for Angry Birds, suggesting the applicability of domain-independent planning to this benchmark AI challenge.