Vidal's libraryTitle: | Multi-Dimensional, MultiStep Negotiation for Task Allocation in a Cooperative System |
Author: | Xiaoqin Zhang, Victor Lesser, and Rodion Podorozhny |
Journal: | Autonomous Agents and Multi-Agent Systems |
Volume: | 10 |
Number: | 1 |
Pages: | 5--40 |
Year: | 2005 |
Abstract: | We present a multi-dimensional, multi-step negotiation mechanism for task allocation among cooperative agents based on distributed search. This mechanism uses marginal utility gain and marginal utility cost to structure this search process, so as to find a solution that maximizes the agents' combined utility. These two utility values together with temporal constraints summarize the agents' local information and reduce the communication load. This mechanism is anytime in character: by investing more time, the agents increase the likelihood of getting a better solution. We also introduce a multiple attribute utility function into negotiations. This allows agents to negotiate over the multiple attributes of the commitment, which produces more options, making it more likely for agents to find a solution that increases the global utility. A set of protocols are constructed and the experimental result shows a phase transition phenomenon as the complexity of negotiation situation changes. A measure of negotiation complexity is developed that can be used by an agent to choose an appropriate protocol, allowing the agents to explicitly balance the gain from the negotiation and the resource usage of the negotiation. |
Cited by 13 - Google Scholar
@Article{zhang05b,
author = {Xiaoqin Zhang and Victor Lesser and Rodion
Podorozhny},
title = {Multi-Dimensional, MultiStep Negotiation for Task
Allocation in a Cooperative System},
journal = {Autonomous Agents and Multi-Agent Systems},
year = 2005,
volume = 10,
number = 1,
pages = {5--40},
abstract = {We present a multi-dimensional, multi-step
negotiation mechanism for task allocation among
cooperative agents based on distributed search. This
mechanism uses marginal utility gain and marginal
utility cost to structure this search process, so as
to find a solution that maximizes the agents'
combined utility. These two utility values together
with temporal constraints summarize the agents'
local information and reduce the communication
load. This mechanism is anytime in character: by
investing more time, the agents increase the
likelihood of getting a better solution. We also
introduce a multiple attribute utility function into
negotiations. This allows agents to negotiate over
the multiple attributes of the commitment, which
produces more options, making it more likely for
agents to find a solution that increases the global
utility. A set of protocols are constructed and the
experimental result shows a phase transition
phenomenon as the complexity of negotiation
situation changes. A measure of negotiation
complexity is developed that can be used by an agent
to choose an appropriate protocol, allowing the
agents to explicitly balance the gain from the
negotiation and the resource usage of the
negotiation.},
keywords = {multiagent negotiation distributed-search},
url = {http://jmvidal.cse.sc.edu/library/zhang05b.pdf},
cluster = {15429239047087695224}
}
Last modified: Wed Mar 9 10:16:28 EST 2011