Consumer behaviour in the mobile telecommunications’ market: The individual’s adoption decision of innovative services

Research in the adoption of innovative services in mobile communications markets has not offered a comprehensive explanation of how the individual makes a choice. This article proposes a theoretical framework for the analysis of the adoption decision of innovative mobile services such as mobile TV. The decision to adopt the service can be viewed as a choice based on two cognitive processes of reasoning and referencing, as postulated in behavioural decision making. The framework has both theoretical and practical value. From a theoretical perspective, it illustrates the manner in which referencing and reasoning influence the individual’s decision to adopt innovative services in the mobile telecommunications market. From a practical perspective the framework offers a market analysis tool which can generate useful insights for the vendors.     

基于逻辑的乳腺癌诊断与治疗专家系统

本系统收集了天津市领域专家的经验,由编译性人工智能语言TurboProlog实现,共由七个模块-知识率、推理机构、解释机构、半自动人工知识获取、知识库管理、动态数据库和人机接口组成。本系统利用产生式规则表达知识,知识库中有多达1242条规则。在推理机构中,采用两类策略:启发式正向和反向推理方法,并利用黑板结构作为公共动态数据区。。提供了半自动人工知识获取以能获取新知识、丰富和更新知识库。在黑板上保留的推理进程是解释机构的素材。总系统共占用118KB内存,在IBM PC AT微机上运行正常,起到了总结和推广领域专家诊断与治疗乳腺癌经验的作用。     

A purely reactive navigation scheme for dynamic environments using Case-Based Reasoning

This paper presents a new sonar based purely reactive navigation technique for mobile platforms. The method relies on Case-Based Reasoning to adapt itself to any robot and environment through learning, both by observation and self experience. Thus, unlike in other reactive techniques, kinematics or dynamics do not need to be explicitly taken into account. Also, learning from different sources allows combination of their advantages into a safe and smooth path to the goal. The method has been succesfully implemented on a Pioneer robot wielding 8 Polaroid sonar sensors. Cristina Urdiales is a Lecturer at the Department of Tecnología Electrónica (DTE) of the University of Málaga (UMA). She received a MSc degree in Telecommunication Engineering at the Universidad Politécnica de Madrid (UPM) and her Ph.D. degree at University of Málaga (UMA). Her research is focused on robotics and computer vision. E.J. Pérez was born in Barcelona, Spain, in 1974. He received his title of Telecommunication Engineering from the University of Málaga, Spain, in 1999. During 1999 he worked in a research project under a grant by the Spanish CYCIT. From 2000 to the present day he has worked as Assistant Professor in the Department of Tecnología Electrónica of the University of Málaga. His research is focused on robotics and artificial vision. Javier Vázquez-Salceda is an Associate Researcher of the Artificial Intelligence Section of the Software Department (LSI), at the Technical University of Catalonia (UPC). Javier obtained an MSc degree in Computer Science at UPC. After his master studies he became research assistant in the KEMLg Group at UPC. In 2003 he presented his Ph.D. dissertation (with honours), which has been awarded with the 2003 ECCAI Artificial Intelligence Dissertation Award. The dissertation has been also recently published as a book by Birkhauser-Verlag. From 2003 to 2005 he was researcher in the Intelligent Systems Group at Utrecht University. Currently he is again member of the KEMLg Group at UPC. His research is focused on theoretical and applied issues of Normative Systems, software and physical agents' autonomy and social control, especially in distributed applications for complex domains such as eCommerce or Medicine. Miquel Sànchez-Marrè (Barcelona, 1964) received a Ph.D. in Computer Science in 1996 from the Technical University of Catalonia (UPC). He is Associate Professor in the Computer Software Department (LSI) of the UPC since 1990 (tenure 1996). He was the head of the Artificial Intelligence section of LSI (1997–2000). He is a pioneer member of International Environmental Modelling and Software Society (IEMSS) and a board member of IEMSS also, since 2000. He is a member of the Editorial Board of International Journal of Applied Intelligence, since October 2001. Since October 2004 he is Associate Editor of Environmental Modelling and Software journal. His main research topics are case-based reasoning, machine learning, knowledge acquisition and data mining, knowledge engineering, intelligent decision-support systems, and integrated AI architectures. He has an special interest on the application of AI techniques to Environmental Decision Support Systems. Francisco Sandoval was born in Spain in 1947. He received the title of Telecommunication Engineering and Ph.D. degree from the Technical University of Madrid, Spain, in 1972 and 1980, respectively. From 1972 to 1989 he was engaged in teaching and research in the fields of opto-electronics and integrated circuits in the Universidad Politécnica de Madrid (UPM) as an Assistant Professor and a Lecturer successively. In 1990 he joined the University of Málaga as Full Professor in the Department of Tecnología Electrónica. He is currently involved in autonomous systems and foveal vision, application of Artificial Neural Networks to Energy Management Systems, and in Broad Band and Multimedia Communication.     

TCBR中案例相关度及案例冗余处理模型

首先介绍基于文档的案例推理技术TCBR,以及CRN、IEs等基本理论,然后详细介绍CRN的构建过程和相关的函数模型,并提出一个求和函数来计算案例与当前问题的相关值,在文章的后面阐述了TCBR的案例学习方法以及案例相似程度的一个判断函数,并提出在这种情况下对案例冗余的处理方案。     

模糊控制节水洗衣机模型

洗衣机的最小用水量涉及洗衣量、肮脏度、衣料质地、洗涤剂、机械力等多种因素，难以建立精确的解析模型进行控制。而采用熟练洗衣者的经验知识构成的模糊推理规则进行控制，则能达到很好的节水效果。本文采用模糊数学的基本原理选择洗衣布量和浑浊度两个主要因素对洗衣机的用水量进行模糊控制，以达到尽可能节水的目的。本模型经过计算机仿真后，所得到的用水量与市场上的几种洗衣机的耗水量相比，具有明显的节水效果，节水量达到10％－20％。     

多值因果图的一种推理算法

该文针对多值因果图存在的两个困难:不严格满足概率论;将其用于实际问题时,推理结果可能出现错误。提出了一种基于因果影响可能性分配的推理算法。该算法对多值因果图进行了补充定义,使多值因果图能够兼容单值因果图;定义了事件变量状态可能性,及其精确计算方法和近似计算方法;给出了将多值因果图转化为单值因果图的方法和步骤,并推导了相应的计算公式;给出了收到证据后,感兴趣变量状态的后验概率计算方法。以核电站二回路系统中蒸汽发生器故障诊断因果图为例,展示了该算法推理计算的全过程。实例表明,该算法能够有效地克服多值因果图存在的困难,其推理过程严谨,计算结果符合实际情况。     

飞机故障诊断专家系统的软件实现

首先提出了开发飞机故障诊断专家系统的必要性 ,并简要说明了笔者所研制的软件的知识基础和设计与实现方法 ;介绍了基于事例的推理 (Case BasedReasoning ,CBR)的优越性 ,重点说明了该软件中事例的来源和事例库的构造 ;并详细介绍了自组织特征映射网络 (SelfOrganizingMap,SOM) ,以及其在该软件中的实现算法和程序结构简图 ;最后分析了该软件广泛的应用前景。     

The role of abduction in chance discovery

Recently, researches on discovery science and knowledge discovery have been carried out in various fields. Basically they are types of learning that learn tendencies from the sets of data of the same or similar categories. In this sense, discovery is to discover the tendencies. As a result, they cannot predict the events that are different from the trend. On the other hand, abduction is thought of as an explanatory reasoning. Indeed, abduction is a reasoning to generate hypotheses to explain an observation. However, the original meaning of abduction was to discover new things that cannot be known in a simple way. In this paper, abduction is defined using the original definition that discovers something that cannot be easily predicted. Then, this paper shows a role of abduction that can suggest or foresee the events that are different from the trend. In fact, Abductive Analogical Reasoning that can generate new hypotheses is adopted to solve the problem. Akinori Abe, Ph.D.: He obtained his Doctor of Engineering (Ph.D) from the University of Tokyo in 1991, with a thesis entitledA Fast Hypothetical Reasoning System using Analogical Case. His main research interests are abduction (hypothetical reasoning), analogical reasoning, chance dicovery and language sense processing. He is a member of the Planning Committee of the New Generation Computing. He worked in NTT MSC (Malaysia) from 2000 to 2002. Currently, he works in ATR.     

Distance semantics for database repair

In many scenarios, a database instance violates a given set of integrity constraints. In such cases, it is often required to repair the database, that is, to restore its consistency. A primary motif behind the repairing approaches is the principle of minimal change, which is the aspiration to keep the recovered data as faithful as possible to the original (inconsistent) database. In this paper, we represent this qualitative principle quantitatively, in terms of distance functions and some underlying metrics, and so introduce a general framework for repairing inconsistent databases by distance-based considerations. The uniform way of representing repairs and their semantics clarifies the essence behind several approaches to consistency restoration in database systems, helps to compare the underlying formalisms, and relates them to existing methods of defining belief revision operators, merging data sets, and integrating information systems.      

On the selection of variables for qualitative modelling of dynamical systems

Behavioural modelling of physical systems from observations of their input/output behaviour is an important task in engineering. Such models are needed for fault monitoring as well as intelligent control of these systems. The paper addresses one subtask of behavioural modelling, namely the selection of input variables to be used in predicting the behaviour of an output variable. A technique that is well suited for qualitative behavioural modelling and simulation of physical systems is Fuzzy Inductive Reasoning (FIR), a methodology based on General System Theory. Yet, the FIR modelling methodology is of exponential computational complexity, and therefore, it may be useful to consider other approaches as booster techniques for FIR. Different variable selection algorithms: the method of the unreconstructed variance for the best reconstruction, methods based on regression coefficients (OLS, PCR and PLS) and other methods as Multiple Correlation Coefficients (MCC), Principal Components Analysis (PCA) and Cluster analysis are discussed and compared to each other for use in predicting the behaviour of a steam generator. The different variable selection algorithms previously named are then used as booster techniques for FIR. Some of the used linear techniques have been found to be non-effective in the task of selecting variables in order to compute a posterior FIR model. Methods based on clustering seem particularly well suited for pre-selecting subsets of variables to be used in a FIR modelling and simulation effort.