# Automated Process Planning Based on a Semantic Capability Model and SMT

arXiv: https://arxiv.org/abs/2312.08801 · HTML: https://mlsanity.com/paper/2312.08801/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 14 Dec 2023.

## Builds on (most cited first, 4 of 4)

- [A Reference Model for Common Understanding of Capabilities and Skills in Manufacturing](https://mlsanity.com/paper/2209.09632/) (2022)
- [A Research Agenda for AI Planning in the Field of Flexible Production Systems](https://mlsanity.com/paper/2112.15484/) (2021)
- [Flexible Production Systems: Automated Generation of Operations Plans Based on ISA-95 and PDDL](https://mlsanity.com/paper/1911.05481/) (2019)
- [Modeling and Executing Production Processes with Capabilities and Skills using Ontologies and BPMN](https://mlsanity.com/paper/2204.09472/) (2022)

## Built on by (most cited first, 1 of 1)

- [An LLM-Based Assistance System for Intuitive and Flexible Capability-Based Planning](https://mlsanity.com/paper/2605.28666/) (2026)

## Abstract (arXiv)

In research of manufacturing systems and autonomous robots, the term capability is used for a machine-interpretable specification of a system function. Approaches in this research area develop information models that capture all information relevant to interpret the requirements, effects and behavior of functions. These approaches are intended to overcome the heterogeneity resulting from the various types of processes and from the large number of different vendors. However, these models and associated methods do not offer solutions for automated process planning, i.e. finding a sequence of individual capabilities required to manufacture a certain product or to accomplish a mission using autonomous robots. Instead, this is a typical task for AI planning approaches, which unfortunately require a high effort to create the respective planning problem descriptions. In this paper, we present an approach that combines these two topics: Starting from a semantic capability model, an AI planning problem is automatically generated. The planning problem is encoded using Satisfiability Modulo Theories and uses an existing solver to find valid capability sequences including required parameter values. The approach also offers possibilities to integrate existing human expertise and to provide explanations for human operators in order to help understand planning decisions.