Multiagent negotiation on multiple issues with incomplete information

Ronghuo Zheng; Tinglong Dai; Nilanjan Chakraborty; Katia Sycara

Multiagent negotiation on multiple issues with incomplete information

Ronghuo Zheng
, Tinglong Dai
, Nilanjan Chakraborty
, Katia Sycara

Mechanical Engineering

Stony Brook University

Carnegie Mellon University

Research output: Contribution to conference › Paper › peer-review

16 Scopus citations

Abstract

We present a reactive offer generation method for general multi-agent multi-attribute negotiation, where the agents have non-linear utility functions and no information about the utility functions of other agents. We prove the convergence of the proposing method and characterize the convergence rate under a finite negotiation time. We also prove that rational agents do not have any incentive to deviate from the proposed strategy. We further present simulation results to demonstrate that on randomly generated problem instances the solution obtained from our protocol is quite close to the Nash bargaining solution.

Original language	English
Pages	1279-1280
Number of pages	2
State	Published - 2013
Event	12th International Conference on Autonomous Agents and Multiagent Systems 2013, AAMAS 2013 - Saint Paul, MN, United States Duration: May 6 2013 → May 10 2013

Conference

Conference	12th International Conference on Autonomous Agents and Multiagent Systems 2013, AAMAS 2013
Country/Territory	United States
City	Saint Paul, MN
Period	05/6/13 → 05/10/13

Keywords

Convergence
Incomplete Information
Multiagent Negotiation

Cite this

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title = "Multiagent negotiation on multiple issues with incomplete information",

abstract = "We present a reactive offer generation method for general multi-agent multi-attribute negotiation, where the agents have non-linear utility functions and no information about the utility functions of other agents. We prove the convergence of the proposing method and characterize the convergence rate under a finite negotiation time. We also prove that rational agents do not have any incentive to deviate from the proposed strategy. We further present simulation results to demonstrate that on randomly generated problem instances the solution obtained from our protocol is quite close to the Nash bargaining solution.",

keywords = "Convergence, Incomplete Information, Multiagent Negotiation",

author = "Ronghuo Zheng and Tinglong Dai and Nilanjan Chakraborty and Katia Sycara",

year = "2013",

language = "English",

pages = "1279--1280",

note = "12th International Conference on Autonomous Agents and Multiagent Systems 2013, AAMAS 2013 ; Conference date: 06-05-2013 Through 10-05-2013",

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TY - CONF

T1 - Multiagent negotiation on multiple issues with incomplete information

AU - Zheng, Ronghuo

AU - Dai, Tinglong

AU - Chakraborty, Nilanjan

AU - Sycara, Katia

PY - 2013

Y1 - 2013

N2 - We present a reactive offer generation method for general multi-agent multi-attribute negotiation, where the agents have non-linear utility functions and no information about the utility functions of other agents. We prove the convergence of the proposing method and characterize the convergence rate under a finite negotiation time. We also prove that rational agents do not have any incentive to deviate from the proposed strategy. We further present simulation results to demonstrate that on randomly generated problem instances the solution obtained from our protocol is quite close to the Nash bargaining solution.

AB - We present a reactive offer generation method for general multi-agent multi-attribute negotiation, where the agents have non-linear utility functions and no information about the utility functions of other agents. We prove the convergence of the proposing method and characterize the convergence rate under a finite negotiation time. We also prove that rational agents do not have any incentive to deviate from the proposed strategy. We further present simulation results to demonstrate that on randomly generated problem instances the solution obtained from our protocol is quite close to the Nash bargaining solution.

KW - Convergence

KW - Incomplete Information

KW - Multiagent Negotiation

UR - https://www.scopus.com/pages/publications/84899407910

M3 - Paper

SP - 1279

EP - 1280

T2 - 12th International Conference on Autonomous Agents and Multiagent Systems 2013, AAMAS 2013

Y2 - 6 May 2013 through 10 May 2013

ER -

Multiagent negotiation on multiple issues with incomplete information

Abstract

Conference

Keywords

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