Negotiating with bounded rational agents in environments with incomplete information using an automated agent

Many tasks in day-to-day life involve interactions among several people. Many of these interactions involve negotiating over a desired outcome. Negotiation in and of itself is not an easy task, and it becomes more complex under conditions of incomplete information. For example, the parties do not kn...

Full description

Saved in:
Bibliographic Details
Published inArtificial intelligence Vol. 172; no. 6; pp. 823 - 851
Main Authors Lin, Raz, Kraus, Sarit, Wilkenfeld, Jonathan, Barry, James
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 01.04.2008
Elsevier
Subjects
Applied sciences
Artificial intelligence
Automated agent
Bilateral negotiation
Bounded rationality
Computer science; control theory; systems
Exact sciences and technology
Incomplete information
Learning and adaptive systems
Automated agent
Bilateral negotiation
Incomplete information
Bounded rationality
Role playing
Uncertain system
Multiagent system
Preference
Bargaining
Artificial intelligence
Modeling
Online AccessGet full text
ISSN0004-3702
1872-7921
DOI10.1016/j.artint.2007.09.007

Cover

More Information
Summary:Many tasks in day-to-day life involve interactions among several people. Many of these interactions involve negotiating over a desired outcome. Negotiation in and of itself is not an easy task, and it becomes more complex under conditions of incomplete information. For example, the parties do not know in advance the exact tradeoff of their counterparts between different outcomes. Furthermore information regarding the preferences of counterparts might only be elicited during the negotiation process itself. In this paper we propose a model for an automated negotiation agent capable of negotiating with bounded rational agents under conditions of incomplete information. We test this agent against people in two distinct domains, in order to verify that its model is generic, and thus can be adapted to any domain as long as the negotiators' preferences can be expressed in additive utilities. Our results indicate that the automated agent reaches more agreements and plays more effectively than its human counterparts. Moreover, in most of the cases, the automated agent achieves significantly better agreements, in terms of individual utility, than the human counterparts playing the same role.
ISSN:0004-3702
1872-7921
DOI:10.1016/j.artint.2007.09.007