Design and implementation of a case-based reasoning system for marketing plans

Unstructured intangible experiences and knowledge are usually difficult to represent and instantiate, which engenders the hardship of knowledge transfer and sharing. Past marketing plans are such valuable documents containing strategic planning knowledge and experiences.Case-Based Reasoning (CBR), which consists of retrieving, reusing, revising, and retaining cases, has been proved effective in retrieving information and knowledge from prior situations and being widely researched and applied in a great variety of problem territories.This paper targets at designing a CBR architecture and a method that facilitate the sharing and retrieving of cases of great concern to the marketing personnel. After an intensive survey of CBR methods and applications, a CBR system embedding multi-attribute decision making method, which provides both overall similarity level and similarity level of each selected attribute, is proposed to enhance the adaptation of a new marketing plan. In addition, a multi-attribute gap analysis diagram is developed to visualize the similarity along with the gap between candidate and target cases, so as to better support interaction and group decision making in the process of strategically formulating a new marketing plan. The CBR system was implemented and successfully demonstrated on case retrieval of a telecommunication company.     

Reuse machining knowledge for generating process plans of axisymmetric parts based on case-based reasoning

The basic idea of case-based reasoning (CBR) is to retrieve and modify the most relevant prior case to match new requirements. In this paper, a framework for a process planning system for machining of axisymmetric parts using case-based reasoning is introduced. It is composed of four major components: retriever, modifier, simulator, and repairer. When a desired axisymmetric part is to be machined, the proposed system first retrieves the most relevant case from the case library as a plan candidate. Since the plan candidate is rarely the same as the desired one, the system performs modification, simulation, and reparation on it. The proposed system has been implemented on a Sun workstation using the ACIS geometric modeler and C++.     

Machining process planning of prismatic parts using case-based reasoning and past process knowledge

A process planner for three-dimension prismatic parts is developed in this paper by utilizing case-based techniques. A three-dimension prismatic part is represented by a set of primary features (such as holes, pockets, slots, etc). The subplan candidates for individual features of a part are first generated by the proposed system via a backward inference planner based on the specifications of cutting tools available in a factory. The system then combines all subplans into the final process plan for a given part based on the merging information. The merging information is the information regarding the manufacturing environment of a factory (i.e. machine layout, transfer line, etc), and plays a key role in the process planning. Generally, the merging information is contained in old plans, and will be extracted by the system using case-based techniques. This way, the proposed system can generate a practical process plan for a given part based on case histories provided by the factory itself. The proposed process planner is composed of five major components: feature indexer, retriever, modifier, simulator, and repairer. It is implemented on a Sun workstation using the ACIS geometric modeler and C++ .     

Using case-based reasoning to establish a continuing care information system of discharge planning

As family structure changes, population is aging and disease styles tend to be chronic, long-term care in Taiwan becomes problematic, needs to be addressed, and requires immediate solution. Presently, many medical care institutions in the country have assisted patients in discharge planning; however, the lack of a standard evaluation procedure in the process of discharge planning is disturbing for each hospital. Without it, there might be huge differences in the evaluation results. Moreover, the lack of support and the uncertainty of case eligibility standard in these institutions further affect the performance of continuing care services.

This research adopted Case-Based Reasoning to establish a continuing care information system of discharge planning. With previously evaluated information of past cases, the similarity index is compared among new cases. In coordinate with Analytic Hierarchy Process, index weight is calculated to reason an old case that is most closely related to the condition of the new case. This information system can assist discharge-planning staff in accurately formulating a plan of action based on previous case-assessment experience and in obtaining valuable information that helps make decision. Through the implementation of the system, accumulation on knowledge and experience of continuing care models will help staff evaluate process of discharge planning to achieve a reasonable, standardized, and simplified procedure as a whole.

This research will transform the evaluating experience of discharge-planning professionals into an assessment method with the application of computer reasoning to make the evaluation process of discharge planning convenient as well as to save more time for discharge-planning professionals to further understand the actual conditions of each case. On the other hand, this information system will provide discharge-planning staff with a set of recommendations as references for making individual discharge plan. It is expected through this research that each hospital be provided with a blue print of improvement in case evaluation process and management.     

Indexing and retrieval in machining process planning using case-based reasoning

To reduce the manufacturing time of a product, one effective way to develop a machining process plan for a new part is to retrieve a relevant case of process planning similar to a new desired part and then adapt the retrieved case to meet the new requirements. This paper proposes a mechanism for retrieval of process planning cases. The core of the retrieval mechanism contains: (1) a feature-based representation of a part and cutting processes; (2) indexing of a part; (3) a feature hierarchical structure based on cutting processes; and (4) a similarity metric used to measure the similarity between a new desired part and any old part in the case base. The application domain here is for axisymmetric part machining. A prototype based on the retrieval mechanism is implemented on a Sun workstation using the ACIS 3D-Toolkit from Spatial Technology Inc.     

自主式水下机器人全局路径规划的基于案例的学习算法研究

本文讨论了基于案例的学习方法在水下机器人全局路径规划中的应用问题．基于案例的学习方法是一种增量式的学习过程，它根据过去的经验进行学习及问题求解．本文对基于案例的学习方法在规划中的应用框架进行了初步研究，对案例属性的提取，案例的匹配和择优，以及案例库的更新等问题提出了相应的算法．最后给出了几组仿真结果．     

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.     

大规模定制环境下的决策案例库系统

针对大规模定制决策的特点,为实现决策案例的重用,提出了一种面向大规模定制决策问题的案例库系统,设计了基于框架结构的案例知识表示模型,介绍了基于模糊加权的案例相似度计算和匹配算法,该算法解决了大规模定制决策问题的结构化表达、检索匹配和重用问题,开发了案例库的原型系统以支持案例管理、推理和基于案例的规则发现。     

范例推理中面向对象的范例表示研究

结合范例推理的具体特点,按照面向对象的形式定义,给出范例的面向对象表示方法.通过类的层次信息,方便地表示范例复杂的结构信息;它一方面克服了属性-值对表示法无法表示复杂结构范例的缺陷,另一方面将领域知识通过约束规则与范例的表示有机结合起来.在范例相似性计算上,区分类内相似性和类间相似性,使得相似性表示尽可能准确和科学,从而有效地拓展了范例推理系统中的知识表示能力.     

A knowledge-based intelligent decision support system for national defense budget planning

In this paper, we present a new framework for knowledge-based intelligent decision support systems for developing a national defense budget planning. The planning procedure for and architecture of the national defense budget in Taiwan are discussed in detail. In particular, the theories and techniques of intelligent decision support are used in the yearly practical budget planning process. Based on data in the financial database and knowledge in the knowledge base, we easily adjust the beforehand budget proposal. Furthermore, a knowledge-based intelligent decision support system has been implemented and it collects a series of rules extracted from national defense experts for successful reasoning. By using forward reasoning and knowledge rules, the system can automatically change and regenerate the national defense budget plan immediately. Finally, the empirical functions of the KIDSS system are also addressed.     

A strategic knowledge-based planning system for freight forwarding industry

In recent years, over half of the Hong Kong freight forwarding firms experienced a decline in business volume due to the growing challenge from the neighbour ports of Yantian and Shekou in Shenzhen, China, which operated in a much cheaper mode. In order to remain competitive, local freight forwarders in Hong Kong must establish a long-term union relationship with their customers such as the provision of customized logistics services. One of the ways is through the use of a co-loading shipment plan, which is a knowledge intensive and complex process involving multiple knowledge source and decision rules. This paper presents hybrid knowledge and model system, which integrates mathematical models with knowledge rules, in the formulation of such co-loading shipment plans. A strategic knowledge-based planning system, (SKPS) integrates knowledge rules with mathematical model for solving problems of co-loading shipment plans formulation and market constriction prevention, is proposed. The system was implemented in Elite World Logistics Service Limited (EW), a local freight forwarding company, for supporting the planning process of a co-loading shipping plan. The result reveals that both customer retention rate and resource utilization has increased significantly.     

Competence Models and the Maintenance Problem

Case-based reasoning (CBR) systems solve problems by retrieving and adapting the solutions to similar problems that have been stored previously as a case base of individual problem solving episodes or cases. The maintenance problem refers to the problem of how to optimize the performance of a CBR system during its operational lifetime. It can have a significant impact on all the knowledge sources associated with a system (the case base, the similarity knowledge, the adaptation knowledge, etc.), and over time, any one, or more, of these knowledge sources may need to be adapted to better fit the current problem-solving environment. For example, many maintenance solutions focus on the maintenance of case knowledge by adding, deleting, or editing cases. This has lead to a renewed interest in the issue of case competence, since many maintenance solutions must ensure that system competence is not adversely affected by the maintenance process. In fact, we argue that ultimately any generic maintenance solution must explicitly incorporate competence factors into its maintenance policies. For this reason, in our work we have focused on developing explanatory and predictive models of case competence that can provide a sound foundation for future maintenance solutions. In this article we provide a comprehensive survey of this research, and we show how these models have been used to develop a number of innovative and successful maintenance solutions to a variety of different maintenance problems.     

一种CBR与RBR相结合的快速预案生成系统

将范例推理(case based reasoning,CBR)与规则推理(rule based reasoning,RBR)两种人工智能技术相结合,实现一种快速预案生成系统.它有效地解决了单纯RBR系统在预案生成过程中的时间延迟缺陷和知识库难以获取的瓶颈.通过CBR工具,能够把以前发生的紧急事件和解决方案生成预案.一旦新的事件发生,首先从预案库中进行案例的相似性检索,如果没有检索到预案或者检索到的预案匹配度很低,再采用RBR系统对紧急事件进行规则推理,然后把推理结果重新存入预案库.实验数据表明,这种方法对单纯RBR系统在时间响应上进行了有效的优化.另外,因为案例的获取比专家系统推理规则的获取容易得多,它同时解决了RBR系统推理规则难以获取的瓶颈.根据这种思想,实现了CBR与RBR结合的快速预案生成系统.目前,它已经应用到抗洪抢险的预案生成和城市应急联动的决策支持上,效果表明它在预案生成速度以及实际可操作性上都具有明显优势.     

A case-based reasoning approach for automating disassembly process planning

One of the first processes for preparing a product for reuse, remanufacture or recycle is disassembly. Disassembly is the process of systematic removal of desirable constituents from the original assembly so that there is no impairment to any useful component. As the number of components in a product increases, the time required for disassembly, as well as the complexity of planning for disassembly rises. Thus, it is important to have the capability to generate disassembly process plans quickly in order to prevent interruptions in processing especially when multiple products are involved. Case-based reasoning (CBR) approach can provide such a capability. CBR allows a process planner to rapidly retrieve, reuse, revise, and retain solutions to past disassembly problems. Once a planning problem has been solved and stored in the case memory, a planner can retrieve and reuse the product's disassembly process plan at any time. The planner can also adapt an original plan for a new product, which does not have an existing plan in case memory. Following adaptation and application, a successful plan is retained in the case memory for future use. This paper presents the procedures to initialize a case memory for different product platforms, and to operate a CBR system, which can be used to plan disassembly processes. The procedures are illustrated using examples.     

Incremental backpropagation learning networks

How to learn new knowledge without forgetting old knowledge is a key issue in designing an incremental-learning neural network. In this paper, we present a new incremental learning method for pattern recognition, called the "incremental backpropagation learning network", which employs bounded weight modification and structural adaptation learning rules and applies initial knowledge to constrain the learning process. The viability of this approach is demonstrated for classification problems including the iris and the promoter domains.     

Ontological emergence scheme in self-organized and emerging systems

This paper presents the concept of “Ontological Emergence”, a process that seeks to adapt an ontology to the changes and new components in a self-organized and emergent system, through the application of a set of rules that allows the emergence of a new conceptualization (emerging concepts). The Ontological Emergence provides the structuration of the information and knowledge that could be generated in the system, creating conceptual models that can adequately represent the new behavior that is emerging. It arises from the need to represent ontologically a conceptualization of a reality that is dynamic, which cannot be pre-defined or pre-determined, in order to generate emerging knowledge models that follows the scalability and the evolution of it. In that sense, in this paper is proposed an “Ontological Emergence Scheme” based on a set of processes of registration, monitoring, analysis and adaptation of the various conceptual models that interact in the system, as well as on some processing rules in regard to requirements and information of the context, in order to allow the ontological emergence. In this proposal scheme, the Meta-ontologies guide the ontological emergence process through the definition of general categories, to facilitate the integration of concepts from different ontologies or data sources. Finally, the paper presents some case studies, showing its utility in self-organized and emergent systems.     

Towards Automated Case Knowledge Discovery in the M 2 Case-Based Reasoning System

In this paper we present the M ² Case-Based Reasoning (CBR) system. The M ² system addresses a number of issues that present methodologies for CBR systems have shied away from. We discuss techniques for removing the knowledge acquisition bottleneck when acquiring case knowledge. Here, case knowledge refers to the complementary knowledge structures, cases (more specific in nature) and adaptation rules (more general). We address the use of negative cases for updating the case knowledge as well as for refining the similarity measures. In particular we discuss in detail, showing experimental results, the use of Data Mining within the M ² system to build the case base from a database containing operational data, and discover adaptation rules. A methodology to monitor the competence of the CBR system and to utilize negative cases for updating the CBR system to enhance its competence is also discussed. The M ² CBR system also employs Rough Set and Fuzzy Set theories to further enhance its capabilities within real-world applications as well as providing a richer and truer model of human reasoning.     

A symbol-based intelligent control system with self-exploration process

This paper presents a symbol-based intelligent control system (SyICS) with a self-exploration process. The SyICS is comprised of a symbolic controller, a percepter, and a self-adaptor, and is a rule-based control system with on-line parametric adaptation. The symbolic controller consists of a number of symbolic rules, such as IF–THEN rules, for controlling the plant. The percepter is a sensory mechanism to perceive the control efficiency. Once the sensory information is found to be improper, i.e., there is inefficient control, the self-adaptor will be activated; otherwise, the symbolic controller will keep on the controlling assignment. The self-adaptor is an adaptive mechanism to explore the new symbolic rules and update the knowledge base for on-line and real-time adaptation. The self-exploration process is applied for the self-adaptor, and the hybrid genetic algorithm with variable-length chromosome is presented to fulfill the self-exploration process. The advantages of the SyICS are: (1) the symbolic controller is intuitive and easy to implement, and (2) The mechanism of the on-line adaptation is adopted and performed by the efficient hybrid genetic algorithm. A robotic path planning application is used to demonstrate the SyICS approach by comparing it with other intelligent control methods. The simulation results show that the robotic paths of SyICS model are the most efficient for all cases based on the path's efficiency measure.     

A database system of mechanical components based on geometric and topological similarity. Part II: indexing, retrieval, matching, and similarity assessment

This paper presents an approach for retrieving and matching similar designs in a database of mechanical components. The retrieval and matching process is based on the geometric and topological similarity between mechanical components. The process constitutes five steps: (i) transforming the component from the CAD system in STEP format, (ii) building an attributed graph for it, (iii) abstracting the graph into some geometric entities, (iv) retrieving a set of similar graphs based on the abstracted data, and (v) matching the graph of the new design with each graph on the set of similar graphs. This paper addresses the last three steps. Retrieving and matching mechanical parts based on their shape has many applications such as cost estimation and process planning. Matching similar parts and calculating a similarity index for them has applications in manufacturing evaluation, design by case-based reasoning, robotics, and computer integrated manufacturing. Having a database system of mechanical components based on part shape serves in all of these applications.