Configuring Multiple Instances with Multi-Configuration

TL;DR

This paper introduces multi-configuration, a new AI-based approach for generating multiple configurations simultaneously, applicable to personalized exams, team formation, and travel planning, expanding traditional single-configuration methods.

Contribution

It presents a novel multi-configuration approach and demonstrates its application to exam configuration, addressing scenarios requiring multiple solutions.

Findings

Proposes a constraint satisfaction problem representation for multi-configuration.

Illustrates applications in exam, team, and travel configurations.

Discusses open issues and future directions for multi-configuration.

Abstract

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.