Abstract
The stable matching problem has many practical applications in two-sided markets, like those that assign doctors to hospitals or students to schools. Usually it is assumed that all agents in each side explicitly express a preference ordering over those in the other side. This can be unfeasible and impractical when the set of agents is very big. However, usually this set has a combinatorial structure, since each agent is often described by some features. To tackle these scenarios, we define a framework for stable matching problems where agents are allowed to express their preferences over those of the other group in a compact way, via soft constraints over the features describing these agents. We focus on a special kind of soft constraints, namely fuzzy constraints. We provide a solving engine for this new kind of stable matching problems that does not increase the time complexity of the classical Gale-Shapley algorithm, while maintaining stability of the matching returned. We then evaluate the approach experimentally.
F. Rossi—On leave from University of Padova.
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Pini, M.S., Rossi, F., Venable, K.B. (2018). Compact Preference Representation via Fuzzy Constraints in Stable Matching Problems: Theoretical and Experimental Studies. In: Ghidini, C., Magnini, B., Passerini, A., Traverso, P. (eds) AI*IA 2018 – Advances in Artificial Intelligence. AI*IA 2018. Lecture Notes in Computer Science(), vol 11298. Springer, Cham. https://doi.org/10.1007/978-3-030-03840-3_16
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