PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Gender-Sensitive Automated Negotiators
Ron Katz and Sarit Kraus
In: AAAI 2007, July 2007, Vancouver, British Columbia Canada.

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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
User Modelling for Computer Human Interaction
ID Code:3846
Deposited By:Sarit Kraus
Deposited On:25 February 2008