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.
@cite_34 determined the successful strategies for different settings using the model proposed by @cite_20 . They employ an evolutionary approach in which strategies and tactics correspond to the genetic material in a genetic algorithm. In their experiments, populations of buyers and sellers with different strategies negotiate in a round robin way. After each round robin round, strategies are evaluated by means of a fitness function. Then, strategies are selected to be the parents of the next population according to their fitness function. In the end, a population of strategies implicitly adapted to the environment is obtained. They use genetic algorithms as a learning mechanism of negotiation strategies when placed under certain circumstances. There are two differences between work and the present work. Firstly, the negotiation model of is designed for linear utility functions. Secondly, the genetic algorithm proposed in this present work is an implicit learning mechanism of the opponent's preferences that guides the offer sampling during the negotiation process.
@cite_34 @cite_20
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To be successful in open, multi-agent environments, autonomous agents must be capable of adapting their negotiation strategies and tactics to their prevailing circumstances. To this end, we present an empirical study showing the relative success of different strategies against different types of opponent in different environments. In particular we adopt an evolutionary approach in which strategies and tactics correspond to the genetic material in a genetic algorithm. We conduct a series of experiments to determine the most successful strategies and to see how and when these strategies evolve depending on the context and negotiation stance of the agent's opponent. Abstract We present a formal model of negotiation between autonomous agents. The purpose of the negotiation is to reach an agreement about the provision of a service by one agent for another. The model defines a range of strategies and tactics that agents can employ to generate initial offers, evaluate proposals and offer counter proposals. The model is based on computationally tractable assumptions, demonstrated in the domain of business process management and empirically evaluated.