Gender-sensitive automated negotiators

Ron Katz, Sarit Kraus

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

This paper introduces an innovative approach for automated negotiating using the gender of human opponents. Our approach segments the information acquired from previous opponents, stores it in two databases, and models the typical behavior of males and of females. The two models are used in order to match an optimal strategy to each of the two subpopulations. In addition to the basic separation, we propose a learning algorithm which supplies an online indicator for the gender separability-level of the population, which tunes the level of separation the algorithm activates. The algorithm we present can be generally applied in different environments with no need for configuration of parameters. Experiments in 4 different one-shot domains, comparing the performance of the gender based separation approach with a basic approach which is not gender sensitive, revealed higher payoffs of the former in almost all the domains. Moreover, using the proposed learning algorithm further improved the results.

Original languageEnglish
Title of host publicationAAAI-07/IAAI-07 Proceedings
Subtitle of host publication22nd AAAI Conference on Artificial Intelligence and the 19th Innovative Applications of Artificial Intelligence Conference
Pages821-826
Number of pages6
StatePublished - 2007
EventAAAI-07/IAAI-07 Proceedings: 22nd AAAI Conference on Artificial Intelligence and the 19th Innovative Applications of Artificial Intelligence Conference - Vancouver, BC, Canada
Duration: 22 Jul 200726 Jul 2007

Publication series

NameProceedings of the National Conference on Artificial Intelligence
Volume1

Conference

ConferenceAAAI-07/IAAI-07 Proceedings: 22nd AAAI Conference on Artificial Intelligence and the 19th Innovative Applications of Artificial Intelligence Conference
Country/TerritoryCanada
CityVancouver, BC
Period22/07/0726/07/07

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