# Intelligent Exploration of Solution Spaces Exemplified by Industrial Reconfiguration Management

arXiv: https://arxiv.org/abs/2207.01693 · HTML: https://mlsanity.com/paper/2207.01693/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 19 references that are in the map.
- First posted on arXiv 4 Jul 2022.

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

- [Enhancing an Intelligent Digital Twin with a Self-organized Reconfiguration Management based on Adaptive Process Models](https://mlsanity.com/paper/2107.03324/) (2021)
- [Transfer Learning as an Enhancement for Reconfiguration Management of Cyber-Physical Production Systems](https://mlsanity.com/paper/2105.14730/) (2021)

## Abstract (arXiv)

Many decision-making approaches rely on the exploration of solution spaces with regards to specified criteria. However, in complex environments, brute-force exploration strategies are usually not feasible. As an alternative, we propose the combination of an exploration task's vertical sub-division into layers representing different sequentially interdependent sub-problems of the paramount problem and a horizontal sub-division into self-sustained solution sub-spaces. In this paper, we present a universal methodology for the intelligent exploration of solution spaces and derive a use-case specific example from the field of reconfiguration management in industry 4.0.