Resource Allocation to Agents with Restrictions: Maximizing Likelihood with Minimum Compromise

Yohai Trabelsi, Abhijin Adiga, Sarit Kraus, S. S. Ravi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations


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 languageEnglish
Title of host publicationMulti-Agent Systems - 19th European Conference, EUMAS 2022, Proceedings
EditorsDorothea Baumeister, Jörg Rothe
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages18
ISBN (Print)9783031206139
StatePublished - 2022
Event19th European Conference on Multi-Agent Systems, EUMAS 2022 - Düsseldorf, Germany
Duration: 14 Sep 202216 Sep 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13442 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference19th European Conference on Multi-Agent Systems, EUMAS 2022

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.


  • Bipartite matching
  • Matching advice
  • Resource allocation
  • Submodular function


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