Playing Angry Birds with a Domain-Independent PDDL+ Planner
Wiktor Piotrowski, Roni Stern, Matthew Klenk, Alexandre Perez, Shiwali, Mohan, Johan de Kleer, Jacob Le

TL;DR
This paper introduces a system that uses a domain-independent PDDL+ planner to play Angry Birds by modeling levels and generating plans, demonstrating comparable performance to specialized methods.
Contribution
First application of domain-independent PDDL+ planning to play Angry Birds, modeling levels and reducing problem complexity for effective planning.
Findings
System's performance matches domain-specific methods
Effective modeling of Angry Birds levels in PDDL+
Demonstrates applicability of domain-independent planning
Abstract
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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsArtificial Intelligence in Games · AI-based Problem Solving and Planning · Model-Driven Software Engineering Techniques
