【PT】[J].【AU:】shambour,QusaiXu, YisiLin, QingZhang, Guangquan【AB】The web provides excellent opportunities to businesses in various aspects ofdevelopment such as finding a business partner online. However, with the rapid growth of web information, business users struggle with information overload and increasingly find it difficult to locate the right information at the right time. Meanwhile, small and medium businesses (SMBs), in particular, are seeking one-to-one e-services from government in current highly competitive markets. How can business users be provided with information and services specific to their needs, rather than an undifferentiated mass of information? An effective solution proposed in this study is the development of personalized e-services. Recommender systems is an effective approach for the implementation of Personalized E-Service which has gained wide exposure in e-commerce in recent years. Accordingly, this paper first presents a hybrid fuzzy semantic recommendation (HFSR) approach which combines item-based fuzzy semantic similarity and item-based fuzzy collaborative filtering (CF) similarity techniques. This paper then presents the implementation of the proposed approach into an intelligent recommendation system prototype called Smart BizSeeker, which can recommend relevant business partners to individual business users,particularly for SMBs. Experimental results show that the HFSR approach can help overcome the semantic limitations of classical CF-based recommendation approaches, namely sparsity and new cold start item problems.【题目】：基于Web的个性化推荐系统使用的业务合作伙伴---模糊语义技术【刊登杂志】: 计算智能【摘要】网站为企业在各方面的发展提供了极好的机会，例如找到一个在线的业务合作伙伴。
然而，随着网络信息的快速增长，商业用户正在和信息过载做斗争，并且在正确的时间找到正确的信息的难度在不断增加。
同时，特别是中小型企业（中小企业），在当前竞争激烈的市场中从政府寻求的是一对一的电子服务。
怎么为企业用户提供他们需要的的信息和服务，而不是一种未分化的海量信息？本文中就为个性化服务发展提出了一个有效的解决方法。
推荐系统是实施个性化的全方位服务的一种有效的方法，近年来在电子商务中得到了广泛的提及。
相应的，本文首先提出了一种混合模糊语义推荐（HFSR）的方法，这种方法结合了基于项目的模糊语义相似度和基于项目的模糊协同过滤（CF）相似的技术。
本文就介绍了在一个智能推荐系统原型中该方法的实现，这个实现方法称为智能bizseeker，它可推荐相关个人商务用户的业务合作伙伴，特别是对中小企业。
实验结果表明，HFSR方法可以帮助克服基于推荐的经典CF语义的限制方法，即稀疏性和冷开始新项目问题。
【PT】[J].【AU】AU Amigoni, FrancescoContinanza, Luca【题目】：基于网格的方法解决多智能体系统中招聘问题【刊登杂志】: 计算智能【摘要】多智能体系统构成的分布式计算和人工智能之间的交叉口的一个独立的课题。
作为算法的技术和多智能体系统的应用已在过去的二十年中持续发展，达到显著的成熟阶段后，许多方法上的问题已经解决了。
本文中我们的目的是通过考虑选择或招聘的问题来帮助该方法的评估，多代理系统代理的一个子集，从一组可用的代理来满足特定的要求。
这个遇到的问题称之为补充的问题，比如在匹配和任务分配中。
我们提出并研究招聘问题的一个新的正式的方法，基于网格的代数形式主义的方法。
由此产生的正式框架可以支持自动招募算法的发展。
【PT】[S].【AU】Zhao, QiangfuBE Madani, KDourado, ARosa, AFilipe, J【摘要】Artificial intelligence (AI) has been a dream of researchers for decades.In 1982, Japan launched the 5th generation computer project,expecting to create AI in computers, but failed. Noting that logic approach alone is not enough, soft computing (e.g. neuro-computing,fuzzy logic and evolutionary computation) has attracted great attention since 1990s. After another 2 decades, however, we have not got any system that is as intelligent as a human, in the sense of "over-all performance". Instead of trying to create intelligence directly, we may try to create "awareness" first, and obtain intelligence "step-by-step".Briefly speaking, awareness is a mechanism for detecting any event which may or may not lead to complete understanding. Depending on the complexity of the events to detect, aware systems can be divided into many levels. Although low level aware systems may not be clever enough to provide understandable knowledge about an observation;they may provide important information for high level aware systems to make understandable decisions. In this paper we do not intend to provide a survey of existing results related to awareness computing. Rather, we will study this field from a new perspective, try to clarify some related terminologies, and propose some problems to solve for creating intelligence through computational awareness.【题目】：计算意识：另一种方式走向智能化【刊登杂志】: 计算智能【SE】Studies in Computational Intelligence【摘要】人工智能(AI)一直是研究人员几十年来的梦想。
1982，日本推出的第五代电子计算机，期待创造人工智能计算机，但失败了。
注意的是，逻辑方法本身是不足够的，自从20世纪90年代以来软计算（例如，神经计算，模糊逻辑和遗传计算）已经引起了极大的关注。
然而又过了20年，我们没有研究出任何向人类智能的智能系统，即意义上的“综合性能”。
我们不是试图去创建直接的智能，而是可能会首先尝试创建“意识”，然后“循序渐进”的获得智能。
简单地说，意识是一种机制，用于检测任何可能的或可能不会完全理解事件。
根据事件的复杂度检测、感知系统可以分成很多层次。
虽然低层次的感知系统可能不足够聪明来提供关于观察可理解的知识，他们可能为高层次感知系统进行理解的决策提供重要的信息。
在本文中，我们不打算提及现有与感知计算相关的成果。
相反，我们将从新的角度对这一领域进行研究，试图阐明一些相关术语，并提出解决的一些通过计算意识来创造智能的问题。
【CT】第三计算智能国际会议【CT】国际复杂分析和潜在的理论会议【CY】月24-26日，2011【CY 】6月20-23日，2011【CL】巴黎，法国【CL】CTR丰富的数学，蒙特利尔，加拿大【PT】[S].【AU】Agarwal, ManishBiswas, Kanad K.Hanmandlu, Madasu【BE】 Madani, KDourado, ARosa, AFilipe, J【AB】 This chapter extends the fuzzy models to the probabilistic domain using the probabilistic fuzzy rules with multiple outputs. The focus has been to effectively model the uncertainty in the real world situations using the extended fuzzy models. The extended fuzzy models capture both the aspects of uncertainty, vagueness and random occurence. We also look deeper into the concepts of fuzzy logic, possibility and probability that sets the background for laying out the mathematical framework for the extended fuzzy models. The net conditional probabilistic possibility is computed that forms the key ingredient in the extension of the fuzzymodels. The proposed concepts are well illustrated through two case-studies of intelligent probabilistic fuzzy systems. The study paves the way for development of computationally intelligent systems that are able to represent the real worldsituations more realistically.【题目】：处理模糊模型的概率域【SE】计算智能研究【刊登杂志】: 计算智能【摘要】本章用具有多个输出的概率模糊规则的模糊模型扩展概率域。