# A Research Agenda for AI Planning in the Field of Flexible Production Systems

arXiv: https://arxiv.org/abs/2112.15484 · HTML: https://mlsanity.com/paper/2112.15484/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 54 references that are in the map.
- First posted on arXiv 31 Dec 2021.

## Builds on (most cited first, 9 of 9)

- [Gated Graph Sequence Neural Networks](https://mlsanity.com/paper/1511.05493/) (2015)
- [Search on the Replay Buffer: Bridging Planning and Reinforcement Learning](https://mlsanity.com/paper/1906.05253/) (2019)
- [Learning Domain-Independent Planning Heuristics with Hypergraph Networks](https://mlsanity.com/paper/1911.13101/) (2019)
- [ASNets: Deep Learning for Generalised Planning](https://mlsanity.com/paper/1908.01362/) (2019)
- [Explainable Planning](https://mlsanity.com/paper/1709.10256/) (2017)
- [Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks](https://mlsanity.com/paper/1804.00823/) (2018)
- [Generalized Planning With Deep Reinforcement Learning](https://mlsanity.com/paper/2005.02305/) (2020)
- [Ontology-Based Skill Description Learning for Flexible Production Systems](https://mlsanity.com/paper/2111.13142/) (2021)
- [Semantic CPPS in Industry 4.0](https://mlsanity.com/paper/2011.11395/) (2020)

## Built on by (most cited first, 8 of 8)

- [Evaluating Large Language Models for Real-World Engineering Tasks](https://mlsanity.com/paper/2505.13484/) (2025)
- [Robustness and Generalization Performance of Deep Learning Models on Cyber-Physical Systems: A Comparative Study](https://mlsanity.com/paper/2306.07737/) (2023)
- [Integrating AI Planning Semantics into SysML System Models for Automated PDDL File Generation](https://mlsanity.com/paper/2506.06714/) (2025)
- [Automated Process Planning Based on a Semantic Capability Model and SMT](https://mlsanity.com/paper/2312.08801/) (2023)
- [Bridging Engineering and AI Planning through Model-Based Knowledge Transformation for the Validation of Automated Production System Variants](https://mlsanity.com/paper/2509.12091/) (2025)
- [On a Uniform Causality Model for Industrial Automation](https://mlsanity.com/paper/2209.09618/) (2022)
- [Avionic Main Fuel Pump Simulation and Fault-Diagnosis Benchmark](https://mlsanity.com/paper/2604.22869/) (2026)
- [From Capability Models to Automated Planning: An AAS-Native Approach for Automatic PDDL Generation](https://mlsanity.com/paper/2606.02167/) (2026)

## Abstract (arXiv)

Manufacturing companies face challenges when it comes to quickly adapting their production control to fluctuating demands or changing requirements. Control approaches that encapsulate production functions as services have shown to be promising in order to increase the flexibility of Cyber-Physical Production Systems. But an existing challenge of such approaches is finding a production plan based on provided functionalities for a demanded product, especially when there is no direct (i.e., syntactic) match between demanded and provided functions. While there is a variety of approaches to production planning, flexible production poses specific requirements that are not covered by existing research. In this contribution, we first capture these requirements for flexible production environments. Afterwards, an overview of current Artificial Intelligence approaches that can be utilized in order to overcome the aforementioned challenges is given. For this purpose, we focus on planning algorithms, but also consider models of production systems that can act as inputs to these algorithms. Approaches from both symbolic AI planning as well as approaches based on Machine Learning are discussed and eventually compared against the requirements. Based on this comparison, a research agenda is derived.