Conference item
Cooperative games with bounded dependency degree
- Abstract:
- Cooperative games provide a framework to study cooperation among self-interested agents. They offer a number of solution concepts describing how the outcome of the cooperation should be shared among the players. Unfortunately, computational problems associated with many of these solution concepts tend to be intractable---NP-hard or worse. In this paper, we incorporate complexity measures recently proposed by Feige and Izsak (2013), called dependency degree and supermodular degree, into the complexity analysis of coopera- tive games. We show that many computational problems for cooperative games become tractable for games whose dependency degree or supermodular degree are bounded. In particular, we prove that simple games admit efficient algorithms for various solution concepts when the supermodular degree is small; further, we show that computing the Shapley value is always in FPT with respect to the dependency degree. Finally, we observe that, while determining the dependency among players is computationally hard, there are efficient algorithms for special classes of games.
- Publication status:
- Published
- Peer review status:
- Reviewed (other)
Actions
Authors
- Publisher:
- Association for the Advancement of Artificial Intelligence Publications
- Host title:
- Thirty-Second AAAI Conference on Artificial Intelligence
- Journal:
- Thirty-Second AAAI Conference on Artificial Intelligence More from this journal
- Pages:
- 1063-1070
- Publication date:
- 2018-04-25
- Acceptance date:
- 2017-12-01
- Event location:
- New Orleans, Louisiana USA
- EISSN:
-
2374-3468
- ISBN:
- 9781577358008
- Pubs id:
-
pubs:834275
- UUID:
-
uuid:59ddaabf-4c42-4b8f-b6ed-0bcd1b8b52e8
- Local pid:
-
pubs:834275
- Source identifiers:
-
834275
- Deposit date:
-
2018-12-10
Terms of use
- Copyright holder:
- Association for the Advancement of Artificial Intelligence
- Copyright date:
- 2018
- Notes:
- © 2018, Association for the Advancement of Artificial Intelligence. This is a conference paper presented at the Thirty-Second AAAI Conference on Artificial Intelligence, 02–07 February 2018, New Orleans, Louisiana USA. This is the accepted manuscript version of the article. The final version is available online from AAAI Publications at: https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/17218
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