Configuring Multiple Instances with Multi-Configuration
This addresses scenarios requiring multiple tailored solutions, like education or team management, but appears incremental as it extends existing configuration methods to multi-instance cases.
The paper tackles the problem of generating multiple configurations simultaneously, such as personalized exams or project teams, and introduces a multi-configuration approach with a constraint satisfaction representation for exam configuration.
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.