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

arXiv: https://arxiv.org/abs/2303.16967 · HTML: https://mlsanity.com/paper/2303.16967/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 23 references that are in the map.
- First posted on arXiv 29 Mar 2023.

## Builds on (most cited first, 2 of 2)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Numerical Integration and Dynamic Discretization in Heuristic Search Planning over Hybrid Domains](https://mlsanity.com/paper/1703.04232/) (2017)

## Built on by (most cited first, 3 of 3)

- [A Domain-Independent Agent Architecture for Adaptive Operation in Evolving Open Worlds](https://mlsanity.com/paper/2306.06272/) (2023)
- [Aligning Perception, Reasoning, Modeling and Interaction: A Survey on Physical AI](https://mlsanity.com/paper/2510.04978/) (2025)
- [Novelty Accommodating Multi-Agent Planning in High Fidelity Simulated Open World](https://mlsanity.com/paper/2306.12654/) (2023)

## Abstract (arXiv)

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.