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