Fast convergence to satisfying distributions
Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence. Pages 1657-1657.
Abstract
We investigate an environment where selfinterested agents have to find high-quality service resources. Agents have common knowledge about resources which are able to provide these services. The performance of resources is measured by the satisfaction obtained by agents using them. The performance of a resource depends on its intrinsic capability and its current load. We use a satisfying rather than an optimizing framework, where agents are content to receive service quality above a threshold. We introduce a formal framework to characterize the convergence of agents to a state where each agent is satisfied with the performance of the service it is currently using. We analyzed the convergence behavior of such a system and identified a mechanism to speed up convergence.
@inproceedings{ijcai2005p338,
title = {Fast convergence to satisfying distributions},
author = {Candale, Teddy and Sen, Sandip},
booktitle = {Proceedings of the Nineteenth International Joint Conference on
Artificial Intelligence, {IJCAI-05}},
publisher = {International Joint Conferences on Artificial Intelligence},
pages = {1657--1657},
year = {2005},
url = {https://ijcai.org/proceedings/2005/338},
}
TY - CPAPER TI - Fast convergence to satisfying distributions AU - Candale, Teddy AU - Sen, Sandip T2 - Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, IJCAI-05 PB - International Joint Conferences on Artificial Intelligence PY - 2005 SP - 1657 EP - 1657 UR - https://ijcai.org/proceedings/2005/338 ER -Copied
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