环境智能领域中复杂效用函数双边议价的进化辅助协商模型
Evolutionary-aided negotiation model for bilateral bargaining in Ambient Intelligence domains with complex utility functions
摘要：
环境智能旨在提供个性化的服务以及人与系统之间更轻松的交互方式。由于多个用户和系统可能在这些环境中共存，因此具有相反偏好的实体很可能需要合作以达到各自的目标。自动协商被认为是解决这类问题的机制之一。在这篇文章中，一个多问题的双边谈判模型的环境智能领域，它是假设代理具有计算有限的资源，不知道他们的对手的喜好。这项工作的主要目标是提供谈判模型，获得有效的协议，同时保持计算成本低。小生境遗传算法是使用前的谈判过程中采样自己的效用函数（自采样）。在谈判过程中，遗传算子被应用于对手和自己的报价，以便对双方感兴趣的新报价进行采样。结果表明，该模型是能够优于相似的竞争力，只有采样前的谈判过程中，并获得类似的结果，以相似的竞争力，有机会获得所有可能的报价。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
