CASE-BASED REASONING FOR KNOWLEDGE ACQUISITION SUGGESTIONS

1994 ◽  
Vol 03 (01) ◽  
pp. 23-45
Author(s):  
LEE BECKER ◽  
TODD GUAY

Case-based suggestion (CBS) is a general mechanism for system-driven interactive knowledge acquisition. CBS applies case-based reasoning to the task of knowledge acquisition. It utilizes previously acquired knowledge embodied in cases to assist the expert during the current knowledge acquisition session. In this work we describe the general CBS technique and illustrate its use during the acquisition of a specific kind of knowledge. A system utilizing CBS was implemented in the acquisition module of a prototype system called ODS, which structures acquired diagnostic knowledge in decision trees. The algorithm used for case-based suggestion by the ODS system and a description of how the decision tree knowledge was represented in the case base is presented. Several evaluation metrics are introduced, and the application of these measures to several experiences of acquiring knowledge with ODS is discussed.

2013 ◽  
Vol 389 ◽  
pp. 698-702
Author(s):  
Xiao Chen ◽  
Ling Chen ◽  
Wo Ye Liu ◽  
Fei Han

To improve the efficiency of planning maintenance resources requirement, the artificial intelligent (AI) technology, especially Case-Based Reasoning (CBR) is applied into maintenance resources requirement analysis process, the process is introduced, and the critical techniques of which, such as case representation and organization etc, are discussed in detail, according to the case characteristics, analyzed the cases main ingredient, cases representation and organization which is based on Relation Database and Object Oriented are detailed discussed, the development of case-based maintenance resources requirement analysis prototype system proved the validity of the technique, formed the foundation for the case-based maintenance resources requirement analysis system perfection.


Author(s):  
Zhan-Song Wang ◽  
Ling Tian ◽  
Yuan-Hao Wu ◽  
Bei-Bei Liu

Existing knowledge provides important reference for designers in mechanical design activities. However, current knowledge acquisition methods based on information retrieval have the problem of inefficiency and low precision, which mainly meet the requirement for knowledge coverage. To improve the efficiency of knowledge acquisition and ensure the availability of design knowledge, this paper proposes a knowledge push service method based on design intent and user interest. First, the design intent model, which is mainly the formal expression of the target function of conceptual design, is built. Second, the user interest model that consists of domain themes and operation logs is built, and an automatic updating method of user interest is proposed. Third, a matching method of design knowledge based on design intent, and a sorting algorithm of knowledge candidates based on user interest are proposed to realize personalized knowledge active push service. Finally, a prototype system called Personalized Knowledge Push System for Mechanical Conceptual Design (MCD-PKPS) is implemented. An illustrative case demonstrates that the proposed method can successfully improve the efficiency and availability of knowledge acquisition.


1997 ◽  
Vol 12 (01) ◽  
pp. 1-40 ◽  
Author(s):  
LEONARD A. BRESLOW ◽  
DAVID W. AHA

Induced decision trees are an extensively-researched solution to classification tasks. For many practical tasks, the trees produced by tree-generation algorithms are not comprehensible to users due to their size and complexity. Although many tree induction algorithms have been shown to produce simpler, more comprehensible trees (or data structures derived from trees) with good classification accuracy, tree simplification has usually been of secondary concern relative to accuracy, and no attempt has been made to survey the literature from the perspective of simplification. We present a framework that organizes the approaches to tree simplification and summarize and critique the approaches within this framework. The purpose of this survey is to provide researchers and practitioners with a concise overview of tree-simplification approaches and insight into their relative capabilities. In our final discussion, we briefly describe some empirical findings and discuss the application of tree induction algorithms to case retrieval in case-based reasoning systems.


2017 ◽  
Vol 7 (1) ◽  
pp. 1
Author(s):  
Edi Faizal

Knowledge acquisition process is not easy, because of the different levels of expertise even though all true. Computer experts had tried other methods to resolve the problem of the acquisition, which is known as case-based reasoning. Representation of knowledge in CBR is a collection of previous case. This research focus is the application of CBR for diagnosing womb diseases. The level of similarity is calculated by using the modified weighted Minkowski. Methods of data collection are interviews, observation and study of literature. The test results show the system can be recognize the womb disease correctly is 94.44% (sensitivity), specitifity rate of 57.14%, PPV of 85.00% and 80.00% NPV. The system have an accuracy rate of 84.00% with an error rate of 16.00%.


Author(s):  
Joel Johansson

For manufacturing companies it is important to develop and produce products that meet requirements from customers and investors. One key factor in meeting these requirements is the efficiency of the product development process. Design automation is a powerful tool to increase efficiency in that process resulting in shortened lead-time, improved product performance, and ultimately decreased cost. Further, automation is beneficial as it increases the ability to adapt products to new product specifications, which is critical to some categories of products. In this paper the retrieval and evaluation processes of the Case Based Reasoning (CBR) method are extended to include shape matching. This enhanced CBR method supports the reuse of existing components when introducing new variants of variant-rich products. The matching method is based on clearance analyzes and is performed during the retrieval of cases and supports the evaluation of suggestions. The method is described along with a prototype-system where the process of selecting components for roof racks for cars is targeted for automation. One specific component of the roof rack is targeted, namely a rubber pad used in the interface between the car roof and the rack.


1997 ◽  
Vol 91 (1) ◽  
pp. 85-101 ◽  
Author(s):  
Takeshi Kohno ◽  
Susumu Hamada ◽  
Dai Araki ◽  
Shoichi Kojima ◽  
Toshikazu Tanaka

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