Year: 2005
Type: conference-paper
Abstract: We describe Utile Coordination, an algorithm that allows a multiagent system to learn where and how to coordinate.The method starts with uncoordinated learners and maintains statistics on expected returns.Coordination dependencies are dynamically added if the statistics indicate a statistically significant benefit.This results in a compact state representation because only necessary coordination is modeled.We apply our method within the framework of coordination graphs in which value rules represent the coordination dependencies between the agents for a specific context.The algorithm is first applied on a small illustrative problem, and next on a large predator-prey problem in which two predators have to capture a single prey.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2400387077