Abstract
We consider the problem of multiple team formation within a project-based university course. Given several tasks with requirements and several students with skills, we investigate the problem of assigning teams of students to tasks as fairly as possible so that each task’s requirements are maximally met. Instead of using traditional team formation techniques, we adapt the fair division formulation by considering tasks as agents and students as items. Furthermore, we present a novel framework that generalizes fair division to account for order within the assignment phase. Finally, we present an algorithm to address instances of team formation within this new setting. Our empirical experiments show that this new algorithm performs better than existing fair division algorithms in terms of speed and fairness, as defined by complete balance ordered and up to one individual.
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Bulmer, J., Fritter, M., Gao, Y., Hui, B. (2020). FASTT: Team Formation Using Fair Division. In: Goutte, C., Zhu, X. (eds) Advances in Artificial Intelligence. Canadian AI 2020. Lecture Notes in Computer Science(), vol 12109. Springer, Cham. https://doi.org/10.1007/978-3-030-47358-7_9
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DOI: https://doi.org/10.1007/978-3-030-47358-7_9
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