Fair Scheduling in Common-Pool Games by Aspiration Learning
(2012) 2012 10th International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks (WiOpt) p.386-390- Abstract
- We propose a distributed learning algorithm for fair scheduling in common-pool games. Common-pool games are strategic-form games where multiple agents compete over utilizing a limited common resource. A characteristic example is the medium access control problem in wireless communications, where multiple users need to decide how to share a single communication channel so that there are no collisions (situations where two or more users use the medium at the same time slot). We introduce a (payoff-based) learning algorithm, namely aspiration learning, according to which agents learn how to play the game based only on their own prior experience, i.e., their previous actions and received rewards. Decisions are also subject to a small... (More)
- We propose a distributed learning algorithm for fair scheduling in common-pool games. Common-pool games are strategic-form games where multiple agents compete over utilizing a limited common resource. A characteristic example is the medium access control problem in wireless communications, where multiple users need to decide how to share a single communication channel so that there are no collisions (situations where two or more users use the medium at the same time slot). We introduce a (payoff-based) learning algorithm, namely aspiration learning, according to which agents learn how to play the game based only on their own prior experience, i.e., their previous actions and received rewards. Decisions are also subject to a small probability of mistakes (or mutations). We show that when all agents apply aspiration learning, then as time increases and the probability of mutations goes to zero, the expected percentage of time that agents utilize the common resource is equally divided among agents, i.e., fairness is established. When the step size of the aspiration learning recursion is also approaching zero, then the expected frequency of collisions approaches zero as time increases. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/2435455
- author
- Chasparis, Georgios LU ; Arapostathis, Ari and Shamma, Jeff S.
- organization
- publishing date
- 2012
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Aspiration learning, medium-access control, common-pool games, resource allocation
- host publication
- 2012 10th International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks (WiOpt)
- editor
- Alpcan, Tansu and Koutsopoulos, Iordanis
- pages
- 386 - 390
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 2012 10th International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks (WiOpt)
- conference location
- Paderborn, Germany
- conference dates
- 2012-05-18
- external identifiers
-
- scopus:84866901588
- ISBN
- 978-1-4673-2294-2
- language
- English
- LU publication?
- yes
- id
- 2ffc4f13-5d2f-4f8e-9f33-f184f69b663b (old id 2435455)
- date added to LUP
- 2016-04-04 10:19:35
- date last changed
- 2022-01-29 20:09:51
@inproceedings{2ffc4f13-5d2f-4f8e-9f33-f184f69b663b,
abstract = {{We propose a distributed learning algorithm for fair scheduling in common-pool games. Common-pool games are strategic-form games where multiple agents compete over utilizing a limited common resource. A characteristic example is the medium access control problem in wireless communications, where multiple users need to decide how to share a single communication channel so that there are no collisions (situations where two or more users use the medium at the same time slot). We introduce a (payoff-based) learning algorithm, namely aspiration learning, according to which agents learn how to play the game based only on their own prior experience, i.e., their previous actions and received rewards. Decisions are also subject to a small probability of mistakes (or mutations). We show that when all agents apply aspiration learning, then as time increases and the probability of mutations goes to zero, the expected percentage of time that agents utilize the common resource is equally divided among agents, i.e., fairness is established. When the step size of the aspiration learning recursion is also approaching zero, then the expected frequency of collisions approaches zero as time increases.}},
author = {{Chasparis, Georgios and Arapostathis, Ari and Shamma, Jeff S.}},
booktitle = {{2012 10th International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks (WiOpt)}},
editor = {{Alpcan, Tansu and Koutsopoulos, Iordanis}},
isbn = {{978-1-4673-2294-2}},
keywords = {{Aspiration learning; medium-access control; common-pool games; resource allocation}},
language = {{eng}},
pages = {{386--390}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{Fair Scheduling in Common-Pool Games by Aspiration Learning}},
url = {{https://lup.lub.lu.se/search/files/5512542/2435538.pdf}},
year = {{2012}},
}