Playing Angry Birds with a Domain-Independent PDDL+ Planner

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.