Coalitional games with stochastic characteristic functions defined by private types

Dengji Zhao, Yiqing Huang, Liat Cohen, Tal Grinshpoun

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

Abstract

This paper studies a coalitional game of task allocation where the characteristic function is not known and it is controlled by some private information from the players. Hence, the challenge here is twofold: (i) incentivize players to reveal their private information truthfully, (ii) incentivize them to collaborate together. Existing reward distribution mechanisms or auctions cannot solve the challenge. Hence, we propose a novel mechanism for the problem from the perspective of both mechanism design and coalitional games.

Original languageEnglish
Title of host publicationProceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020
EditorsBo An, Amal El Fallah Seghrouchni, Gita Sukthankar
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Pages2086-2088
Number of pages3
ISBN (Electronic)9781450375184
StatePublished - 1 Jan 2020
Event19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020 - Virtual, Auckland, New Zealand
Duration: 19 May 2020 → …

Publication series

NameProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Volume2020-May
ISSN (Print)1548-8403
ISSN (Electronic)1558-2914

Conference

Conference19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020
Country/TerritoryNew Zealand
CityVirtual, Auckland
Period19/05/20 → …

Keywords

  • Coalitional games
  • Incentive compatibility
  • Stochastic characteristic functions
  • Task allocation

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering

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