2006 LearningtheTaskAllocationGame

• (Abdallah & Lesser, 2006) ⇒ Sherief Abdallah, and Victor Lesser. (2006). “Learning the Task Allocation Game.” In: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems. ISBN:1-59593-303-4 doi:10.1145/1160633.1160786

Subject Headings: Multi-Agent Learning Algorithm; WPL Algorithm; Multi-Agent Reinforcement Learning Algorithm.

Cited By

• http://scholar.google.com/scholar?q=%222006%22+Learning+the+Task+Allocation+Game
• http://dl.acm.org/citation.cfm?id=1160633.1160786&preflayout=flat#citedby

Quotes

Author Keywords

• Reinforcement Learning; Multiagent Systems; Task Allocation; Markov Decision Process

Abstract

The distributed task allocation problem occurs in domains like web services, the grid, and other distributed systems. In this problem, the system consists of servers and mediators. Servers execute tasks and may differ in their capabilities, e.g. one server may take more time than the other in executing the same task. Mediators act on behalf of users, which can potentially be other mediators, and are responsible for receiving tasks from users and allocating them to servers.This work introduces a new gradient ascent learning algorithm that outperforms state of the art multiagent learners on this problem. We experimentally show that our algorithm converges faster and is less sensitive to tuning parameters than other algorithms. We also provide an informal proof that WPL has the same convergence guarantee as the best known algorithm, GIGA-WoLF. We also show that our algorithm converges in Jordan's and Shapley's games where many other algorithms fail to converge. Finally, we verify the practicality of our algorithm in the distributed task allocation domain, comparing its performance to an optimal global solution.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2006 LearningtheTaskAllocationGame	Victor Lesser Sherief Abdallah			Learning the Task Allocation Game				10.1145/1160633.1160786		2006