# Real-World Planning with PDDL+ and Beyond

arXiv: https://arxiv.org/abs/2402.11901 · HTML: https://mlsanity.com/paper/2402.11901/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 29 references that are in the map.
- First posted on arXiv 19 Feb 2024.

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

- [Plan-based Policies for Efficient Multiple Battery Load Management](https://mlsanity.com/paper/1401.5859/) (2014)
- [Playing Angry Birds with a Domain-Independent PDDL+ Planner](https://mlsanity.com/paper/2107.04635/) (2021)

## Built on by (most cited first, 2 of 2)

- [Integrating Reinforcement Learning, Action Model Learning, and Numeric Planning for Tackling Complex Tasks](https://mlsanity.com/paper/2502.13006/) (2025)
- [Toward PDDL Planning Copilot](https://mlsanity.com/paper/2509.12987/) (2025)

## Abstract (arXiv)

Real-world applications of AI Planning often require a highly expressive modeling language to accurately capture important intricacies of target systems. Hybrid systems are ubiquitous in the real-world, and PDDL+ is the standardized modeling language for capturing such systems as planning domains. PDDL+ enables accurate encoding of mixed discrete-continuous system dynamics, exogenous activity, and many other interesting features exhibited in realistic scenarios. However, the uptake in usage of PDDL+ has been slow and apprehensive, largely due to a general shortage of PDDL+ planning software, and rigid limitations of the few existing planners. To overcome this chasm, we present Nyx, a novel PDDL+ planner built to emphasize lightness, simplicity, and, most importantly, adaptability. The planner is designed to be effortlessly customizable to expand its capabilities well beyond the scope of PDDL+. As a result, Nyx can be tailored to virtually any potential real-world application requiring some form of AI Planning, paving the way for wider adoption of planning methods for solving real-world problems.