Abstract
Many scenarios where agents with restrictions compete for resources can be cast as maximum matching problems on bipartite graphs. Our focus is on resource allocation problems where agents may have restrictions that make them incompatible with some resources. We assume that a Principal chooses a maximum matching randomly so that each agent is matched to a resource with some probability. Agents would like to improve their chances of being matched by modifying their restrictions within certain limits. The Principal’s goal is to advise an unsatisfied agent to relax its restrictions so that the total cost of relaxation is within a budget (chosen by the agent) and the increase in the probability of being assigned a resource is maximized. We establish hardness results for some variants of this budget-constrained maximization problem and present algorithmic results for other variants. We experimentally evaluate our methods on synthetic datasets as well as on two novel real-world datasets: a vacation activities dataset and a classrooms dataset.
Original language | English |
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Title of host publication | Multi-Agent Systems - 19th European Conference, EUMAS 2022, Proceedings |
Editors | Dorothea Baumeister, Jörg Rothe |
Publisher | Springer Science and Business Media Deutschland GmbH |
Pages | 403-420 |
Number of pages | 18 |
ISBN (Print) | 9783031206139 |
DOIs | |
State | Published - 2022 |
Event | 19th European Conference on Multi-Agent Systems, EUMAS 2022 - Düsseldorf, Germany Duration: 14 Sep 2022 → 16 Sep 2022 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 13442 LNAI |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 19th European Conference on Multi-Agent Systems, EUMAS 2022 |
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Country/Territory | Germany |
City | Düsseldorf |
Period | 14/09/22 → 16/09/22 |
Bibliographical note
Publisher Copyright:© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Funding
Acknowledgments. We are grateful to the reviewers of EUMAS 2022 for carefully reading the manuscript and providing valuable suggestions. This work was supported by Israel Science Foundation under grant 1958/20, the EU Project TAILOR under grant 952215, Agricultural AI for Transforming Workforce and Decision Support (AgAID) grant no. 2021-67021-35344 from the USDA National Institute of Food and Agriculture, and the US National Science Foundation grant OAC-1916805 (CINES).
Funders | Funder number |
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CINES | |
National Science Foundation | OAC-1916805 |
National Institute of Food and Agriculture | |
European Commission | 952215, 2021-67021-35344 |
Israel Science Foundation | 1958/20 |
Keywords
- Bipartite matching
- Matching advice
- Resource allocation
- Submodular function