Fast Convergence to Satisfying Distributions

Abstract

We investigate an environment where self-interested 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.

Cite

Text

Candale and Sen. "Fast Convergence to Satisfying Distributions." International Joint Conference on Artificial Intelligence, 2005.

Markdown

[Candale and Sen. "Fast Convergence to Satisfying Distributions." International Joint Conference on Artificial Intelligence, 2005.](https://mlanthology.org/ijcai/2005/candale2005ijcai-fast/)

BibTeX

@inproceedings{candale2005ijcai-fast,
  title     = {{Fast Convergence to Satisfying Distributions}},
  author    = {Candale, Teddy and Sen, Sandip},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2005},
  pages     = {1657-1658},
  url       = {https://mlanthology.org/ijcai/2005/candale2005ijcai-fast/}
}