  Ambient Intelligence aims to offer personalized services and easier ways of
interaction between people and systems. Since several users and systems may
coexist in these environments, it is quite possible that entities with opposing
preferences need to cooperate to reach their respective goals. Automated
negotiation is pointed as one of the mechanisms that may provide a solution to
this kind of problems. In this article, a multi-issue bilateral bargaining
model for Ambient Intelligence domains is presented where it is assumed that
agents have computational bounded resources and do not know their opponents'
preferences. The main goal of this work is to provide negotiation models that
obtain efficient agreements while maintaining the computational cost low. A
niching genetic algorithm is used before the negotiation process to sample
one's own utility function (self-sampling). During the negotiation process,
genetic operators are applied over the opponent's and one's own offers in order
to sample new offers that are interesting for both parties. Results show that
the proposed model is capable of outperforming similarity heuristics which only
sample before the negotiation process and of obtaining similar results to
similarity heuristics which have access to all of the possible offers.
