WCOID-DG: An approach for case base maintenance based on Weighting, Clustering, Outliers, Internal Detection and Dbsan-Gmeans

The success of the Case Based Reasoning system depends on the quality of the case data and the speed of the retrieval process that can be costly in time, especially when the number of cases gets bulky. To guarantee the system?s quality, maintaining the contents of a case base (CB) becomes unavoidably. In this paper, we propose a novel case base maintenance policy named WCOID-DG: Weighting, Clustering, Outliers and Internal cases Detection based on Dbscan and Gaussian means. Our WCOID-DG policy uses in addition to feature weights and outliers detection methods, a new efficient clustering technique, named DBSCAN-GM (DG) which is a combination of DBSCAN and Gaussian-Means algorithms. The purpose of our WCOID-GM is to reduce both the storage requirements and search time and to focus on balancing case retrieval efficiency and competence for a CB. WCOID-GM is mainly based on the idea that a large CB with weighted features is transformed to a small CB with improving its quality. We support our approach with empirical evaluation using different benchmark data sets to show its competence in terms of shrinking the size of the CB and the research time, as well as, getting satisfying classification accuracy.     

A hybrid case adaptation approach for case-based reasoning

Case-Based Reasoning is a methodology for problem solving based on past experiences. This methodology tries to solve a new problem by retrieving and adapting previously known solutions of similar problems. However, retrieved solutions, in general, require adaptations in order to be applied to new contexts. One of the major challenges in Case-Based Reasoning is the development of an efficient methodology for case adaptation. The most widely used form of adaptation employs hand coded adaptation rules, which demands a significant knowledge acquisition and engineering effort. An alternative to overcome the difficulties associated with the acquisition of knowledge for case adaptation has been the use of hybrid approaches and automatic learning algorithms for the acquisition of the knowledge used for the adaptation. We investigate the use of hybrid approaches for case adaptation employing Machine Learning algorithms. The approaches investigated how to automatically learn adaptation knowledge from a case base and apply it to adapt retrieved solutions. In order to verify the potential of the proposed approaches, they are experimentally compared with individual Machine Learning techniques. The results obtained indicate the potential of these approaches as an efficient approach for acquiring case adaptation knowledge. They show that the combination of Instance-Based Learning and Inductive Learning paradigms and the use of a data set of adaptation patterns yield adaptations of the retrieved solutions with high predictive accuracy.     

SYMBAD—Similarity based agents for design

This paper presents the SYMBAD (similarity based agents for design) system, exploring multi-agent aspects in an architecture company, capturing, cataloging, and communicating information produced by the team members. The main task managed by the designers is to build stands to present the image of a company, project its presence in the market and emphasize the corporate identity to all prospects. From conceptual design to the construction of a final product, a stand project passes through many hands, each one adding bits and pieces until it is completed. Reuse of materials and ideas is less feasible as design complexity increases. The processes and problems in stand projects are quite common and can be easily found in other design situations. We present an agent framework to improve process awareness in an architecture company. The agents instrument the process to produce global awareness, to facilitate reuse and optimize the process as a whole. In this paper we present the agent architecture, as well as each agent’s general functioning and reasoning rules.     

Case based reasoning method for computer aided welding fixture design

This paper presents a case-based reasoning (CBR) method for welding fixture design, a critical issue in the manufacturing of large and complicated equipment. However, previous fixture design research has mainly focused on machining fixtures rather than welding fixtures. In this paper, an approach of data abstraction for fixture design information representation is proposed, first to systemize and manage myriads of fixture related resources, e.g., past fixture design solutions, fixture units depository. Based on this approach, a multi-level CBR method for welding fixture design is then presented. This method could help designers, by referencing previous design cases, to make a conceptual fixturing solution quickly and, finally, finish the detailed solution of fixture design.     

A mix method of knowledge capitalization in maintenance

This paper deals with knowledge capitalization in maintenance especially in diagnosis and repair of industrial equipments. The goal is to propose a method of knowledge capitalization in order to develop a decision support system for maintenance operators. The knowledge capitalization cycle was adopted as the underlying principle. It consists of four principal steps: detect, preserve, capitalize and actualize the strategic knowledge. Different knowledge management tools and methods that can be used in the cycle are reviewed. We propose a mix method of knowledge capitalization in maintenance. This method integrates a representation and a reasoning model both completing each other and suitable to represent and manipulate the domain knowledge. The knowledge representation model using unified modelling language (UML) diagram proposes different domain models based on maintenance analysis to guide the domain expertise. The reasoning model uses the case-based reasoning which allows the manipulation of represented domain knowledge. Finally, the method is implemented on the pallet transfer system Sormel in the context of Proteus e-maintenance platform.     

Formulating strategies for stakeholder management: a case-based reasoning approach

The management of competing stakeholders has emerged as an important weapon for strategic management. Typically, reactive, defensive, accommodative, or proactive (RDAP) strategies have been employed for getting into the world of stakeholders. This paper proposes a methodology for formulating strategies for stakeholder management by the use of these RDAP strategies. Our methodology consists of four phases: stakeholder analysis, strategy retrieval, strategy revision, and strategy implementation. Strategies are derived on the basis of similar cases stored in case bases. A system called the stakeholder management strategy support system (SMSS) is implemented to put our methodology to work. In order to demonstrate the practical usefulness of this system, Korean Healthcare IT (Information Technology) industry is illustrated. This illustration implies that our methodology is useful, especially in view of dynamic nature of business and its stakeholders. Our methodology will be able to help any business leader create value while navigating a multi-stakeholder environment.     

An application of knowledge based modelling using scripts

Knowledge management (KM) takes an increasingly significant place in the companies. The field of the KM aims to answer the problems of memory within companies by proposing methodologies to formalise know-how during the different steps of production. The KM is a domain with many ramifications and applications. One of them, so called Knowledge Based Engineering, search how to record knowledge from experts to put them in CAD software. Our study relates to the possibility of using the knowledge of an expert in modelling and, more particularly, on the automatic modelling of filling systems in foundry.     

基于案例推理的工作流异常处理研究

对工作流的异常和案例推理（Case—Based Reasoning，简称CBR）的机制进行了介绍，给出了一个应用CBR技术进行异常处理的工作流模型．并研究了应用CBR方法处理工作流异常的机制。     

A hybrid intelligent system to improve predictive accuracy for cache prefetching

Cache being the fastest medium in memory hierarchy has a vital role to play for fully exploiting available resources, concealing latencies in IO operations, languishing the impact of these latencies and hence in improving system response time. Despite plenty of efforts made, caches alone cannot comprehend larger storage requirements without prefetching. Cache prefetching is speculatively fetching data to restrain all delays. However, effective prefetching requires a strong prediction mechanism to load relevant data with higher degree of accuracy. In order to ameliorate the predictive performance of cache prefetching, we applied the hybrid of two AI approaches named case based reasoning (CBR) and artificial neural networks (ANN). CBR maintains the past experience and ANN are used in adaptation phase of CBR instead of employing static rule base. The novelty of technique in this domain is valued due to hybrid of two approaches as well as usage of suffix tree in populating the CBR’s case base. Suffix trees provide rich data patterns for populating case base and greatly enhanced the overall performance. A number of evaluations from different aspects with varying parameters are presented (along with some findings) where the efficacy of our technique is affirmed with improved predictive accuracy and reduced level of associated costs.     

基于XML的案例表示和案例库构造方法

将基于案例推理(CBR)技术与XML结合,提出了基于XML的案例表示方法,给出了DTD定义,分析了它与传统数据库相比的优势,并以Snort规则的基于XML的案例化为例,证明了该方法的有效性.所做工作为CBR的研究提供了一些新的思路.     

A graphical approach to relational reasoning

Relational reasoning is concerned with relations over an unspecified domain of discourse. Two limitations to which it is customarily subject are: only dyadic relations are taken into account; all formulas are equations, having the same expressive power as first-order sentences in three variables. The relational formalism inherits from the Peirce-Schröder tradition, through contributions of Tarski and many others.Algebraic manipulation of relational expressions (equations in particular) is much less natural than developing inferences in first-order logic; it may in fact appear to be overly machine-oriented for direct hand-based exploitation.The situation radically changes when one resorts to a convenient representation of relations based on labeled graphs. The paper provides details of this representation, which abstracts w.r.t. inessential features of expressions.Formal techniques illustrating three uses of the graph representation of relations are discussed: one technique deals with translating first-order specifications into the calculus of relations; another one, with inferring equalities within this calculus with the aid of convenient diagram-rewriting rules; a third one with checking, in the specialized framework of set theory, the definability of particular set operations. Examples of use of these techniques are produced; moreover, a promising approach to mechanization of graphical relational reasoning is outlined.     

Effects of music tempo and task difficulty on multi-attribute decision-making: An eye-tracking approach

This study examined the effects of music tempo and task difficulty on the performance of multi-attribute decision-making according two alternative perspectives: background music as the arousal inducer vs. the distractor. An eye-tracking based experiment was conducted. Our results supported the arousal inducer perspective that, with the same level of decision time, participants made decisions more accurately with the presentation of faster than slower tempo music. Further, faster tempo music was found to improve the accuracy of harder decision-making only, not that of easier decision-making. More interestingly, our exploratory analysis on eye fixations found the occurrence of adaptive behavior, namely, that the search pattern of participants became more intra-dimensional under the faster tempo music as compared with the slower tempo music.     

An ontological intelligent agent platform to establish an ecological virtual enterprise

A virtual enterprise (VE) is a dynamic alliance of companies collaborating for the accomplishment of a specific business goal. To establish a VE, it is very important for the VE initiator to select appropriate partners. General criteria such as price, lead time, quality, etc. are the major concerns for most VE initiators. However, in today’s environmentally conscious society, environmental issues such as enterprise green image, product eco-design, etc. are increasingly receiving attention. Thus, it is worth to research on how to select the appropriate collaborative partners to establish an ecological VE.The objective of this paper is to establish a multi-agent system platform for individual companies to form an ecological VE based on ontology theory and intelligent agents. The ontological approaches include shared ontology construction, ontology matching, ontology integration, ontology storage and ontology reasoning. In the generalized case that the VE initiator is a manufacturer and the collaborating partner are suppliers, the multi-agent system comprises three types of intelligent agents, namely, knowledge manager agent (KMrA), manufacturer agent (MA) and supplier agent (SA). MA and SA represent the capabilities and interests of the VE initiator and the VE partners, respectively. KMrA is in charge of functioning sub-tasks of the ontological approach. To select partners for the ecological VE, the VE initiator will also consider the environmental criteria, in addition to the general supplier selection criteria such as price, quantity, quality and lead time. The environmental criteria may include factors such as environmental management, green image, green product and pollution control. The complete set of selection criteria, including the environmental criteria, are categorised into quantitative or qualitative criteria. The formation of ecological VE is then divided into two stages, that is, candidate supplier selection based on qualitative criteria, and ultimate supplier selection based on quantitative criteria. A simplified example is introduced to illustrate and justify the proposed ontological approaches and intelligent agent platform.     

From cacophony to harmony: A case study about the IS implementation process as an opportunity for organizational transformation at Sentara Healthcare

The cacophony of criticisms emanating from an organization facing an information technology-enabled transformation can be deafening and deleterious. This is especially true in healthcare in the US, where information systems investments are typically huge and often perceived by change resistant stakeholders as disruptive or even potentially life threatening. We describe how the IS implementation process itself contributed to organizational transformation in terms of changes in coordination, culture, and learning at a successful organization, Sentara Healthcare, which transformed the discordant cacophony of the change process into a harmonious implementation.     

A logical approach to representing and reasoning about space

The need for a formal language in which to express and reason about spatial concepts is of crucial importance in many areas of AI and visual systems. For the last five years, spatial reasoning research by the Qualitative Spatial Reasoning Group, University of Leeds, has centred on the development and application of such a language — the RCC spatial logic. Below, we briefly describe the work of the group in this area.     

On vague logics and approximate reasoning based on vague linear transformation

As a generalisation of the Fuzzy Sets theory, vague set has been proven to be a new tool in dealing with vague information. In this article, we attempt to generalise the techniques of fuzzy inference in a vague environment. In the rule-based inference system, an ‘if?…?then?…’ rule can be considered a transformer that implements information conversion between input–output ends. Thus, according to the logical operations of vague linguistic variables, we introduce an approach to approximation inference based on linear transformation, and then discuss the representations for several inference structures regarding single rule, multi-rules and compound rules. By defining the inclusion function of vague sets, we provide vague rough approximation based on measure of inclusion, and then present a method on rule creation from a decision system. A case study on the prediction for welding deformation is used to illustrate the effectiveness of the proposed approaches.     

基于小波逼近和尺度变换的非参数辨识方法

提出一种以非正交小波为基函数并应用小波多尺度分析的系统脉冲响应辨识方法，该方法以小波级数的形式逼近脉冲响应过程．从理论上证明了经小波尺度变换后系统随机噪声的方差值减小，即噪声得到有效的抑制，从而大大提高了辨识精度．应用实例验证了所得结果的正确性和算法的实用性．     

Prototypical case mining from biomedical literature for bootstrapping a case base

This article addresses the task of mining for cases from biomedical literature to automatically build an initial case base for a case-based reasoning (CBR) system. This research takes place within the Mémoire project, which has for goal to provide a framework to facilitate building CBR systems in biology and medicine. By analyzing medical literature, the ProCaseMiner system mines for medical concepts such as diseases, signs and symptoms, laboratory tests, and treatment plans in relationship with one another, and connects them together in a given medical domain. It then organizes these concepts in a higher-level structure called a case. This case mining component provides a definite help to bootstrap the creation of a biomedical CBR system case base, composed of both concrete cases and prototypical cases. Currently, most cases learnt correspond to prototypical cases, given the level of abstraction of their features. This article validates the approach by presenting a comparison between the prototypical cases learnt from stem-cell transplantation domain with those created by a team of experts in the domain.