# Configuring Multiple Instances with Multi-Configuration

arXiv: https://arxiv.org/abs/2109.09696 · HTML: https://mlsanity.com/paper/2109.09696/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 15 references that are in the map.
- First posted on arXiv 20 Sep 2021.

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

- [An Efficient Diagnosis Algorithm for Inconsistent Constraint Sets](https://mlsanity.com/paper/2102.09005/) (2021)
- [DirectDebug: Automated Testing and Debugging of Feature Models](https://mlsanity.com/paper/2102.05949/) (2021)
- [AI Techniques for Software Requirements Prioritization](https://mlsanity.com/paper/2108.00832/) (2021)
- [KnowledgeCheckR: Intelligent Techniques for Counteracting Forgetting](https://mlsanity.com/paper/2102.07825/) (2021)

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

- [Solving Multi-Configuration Problems: A Performance Analysis with Choco Solver](https://mlsanity.com/paper/2310.02658/) (2023)

## Abstract (arXiv)

Configuration is a successful application area of Artificial Intelligence. In the majority of the cases, configuration systems focus on configuring one solution (configuration) that satisfies the preferences of a single user or a group of users. In this paper, we introduce a new configuration approach - multi-configuration - that focuses on scenarios where the outcome of a configuration process is a set of configurations. Example applications thereof are the configuration of personalized exams for individual students, the configuration of project teams, reviewer-to-paper assignment, and hotel room assignments including individualized city trips for tourist groups. For multi-configuration scenarios, we exemplify a constraint satisfaction problem representation in the context of configuring exams. The paper is concluded with a discussion of open issues for future work.