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.
Later, @cite_16 presented a negotiation strategy for bilateral bargaining that is focused on achieving situations by means of trade-off. The heuristic applied to perform trade-off is similar to that employed in this present work. Given an agent's current utility, the offer from the iso-utility curve that is most similar to the last offer received from the opponent is sent. The idea behind this heuristic is that, since the proposed offer is the most similar to the last offer received from the opponent, it is more likely to be satisfactory to both participants. A fuzzy similarity criterion is employed to compare offers. Nevertheless, the use of fuzzy similarity requires some knowledge of opponent preferences. The application of criteria of this kind is complicated in complex utility functions due to the interdependencies among the different issues. In this present work, the Euclidean distance is used, as this does not require any knowledge about the opponent, and which is independent of the interdependencies among issues.
@cite_16
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Addresses the issues involved in software agents making trade-offs during automated negotiations in which they have information uncertainty and resource limitations. In particular the importance of being able to make tradeoffs in real-world applications is highlighted and an algorithm for performing trade-offs for multi-dimensional goods is developed. The algorithm uses the notion of fuzzy similarity in order to find negotiation solutions that are beneficial to both parties. Empirical results indicate the benefits and effectiveness of the trade-off algorithm in a range of negotiation situations.