scholarly journals Case-based reasoning and system design: An integrated approach based on ontology and preference modeling

2014 ◽  
Vol 28 (1) ◽  
pp. 49-69 ◽  
Author(s):  
Juan Camilo Romero Bejarano ◽  
Thierry Coudert ◽  
Elise Vareilles ◽  
Laurent Geneste ◽  
Michel Aldanondo ◽  
...  
Keyword(s):  
System Design ◽  
Similarity Measures ◽  
Integrated Approach ◽  
System Engineering ◽  
Retrieval Algorithm ◽  
Case Based Reasoning ◽  
Requirements Definition ◽  
Definition Of ◽  
Similar Solutions ◽  
Case Based

AbstractThis paper addresses the fulfillment of requirements related to case-based reasoning (CBR) processes for system design. Considering that CBR processes are well suited for problem solving, the proposed method concerns the definition of an integrated CBR process in line with system engineering principles. After the definition of the requirements that the approach has to fulfill, an ontology is defined to capitalize knowledge about the design within concepts. Based on the ontology, models are provided for requirements and solutions representation. Next, a recursive CBR process, suitable for system design, is provided. Uncertainty and designer preferences as well as ontological guidelines are considered during the requirements definition, the compatible cases retrieval, and the solution definition steps. This approach is designed to give flexibility within the CBR process as well as to provide guidelines to the designer. Such questions as the following are conjointly treated: how to guide the designer to be sure that the requirements are correctly defined and suitable for the retrieval step, how to retrieve cases when there are no available similarity measures, and how to enlarge the research scope during the retrieval step to obtain a sufficient panel of solutions. Finally, an example of system engineering in the aeronautic domain illustrates the proposed method. A testbed has been developed and carried out to evaluate the performance of the retrieval algorithm and a software prototype has been developed in order to test the approach. The outcome of this work is a recursive CBR process suitable to engineering design and compatible with standards. Requirements are modeled by means of flexible constraints, where the designer preferences are used to express the flexibility. Similar solutions can be retrieved even if similarity measures between features are not available. Simultaneously, ontological guidelines are used to guide the process and to aid the designer to express her/his preferences.

Contextual case-based reasoning applied to a mobile device

2017 ◽  
Vol 13 (3) ◽  
pp. 282-299 ◽  
Author(s):  
Djamel Guessoum ◽  
Moeiz Miraoui ◽  
Chakib Tadj
Keyword(s):  
Mobile Device ◽  
Inertial Sensors ◽  
Contextual Information ◽  
Similarity Measures ◽  
Case Based Reasoning ◽  
Two Stage ◽  
Minimal Processing ◽  
Current Case ◽  
Content Type ◽  
Case Based

Purpose This paper aims to apply a contextual case-based reasoning (CBR) to a mobile device. The CBR method was chosen because it does not require training, demands minimal processing resources and easily integrates with the dynamic and uncertain nature of pervasive computing. Based on a mobile user’s location and activity, which can be determined through the device’s inertial sensors and GPS capabilities, it is possible to select and offer appropriate services to this user. Design/methodology/approach The proposed approach comprises two stages. The first stage uses simple semantic similarity measures to retrieve the case from the case base that best matches the current case. In the second stage, the obtained selection of services is then filtered based on current contextual information. Findings This two-stage method adds a higher level of relevance to the services proposed to the user; yet, it is easy to implement on a mobile device. Originality/value A two-stage CBR using light processing methods and generating context aware services is discussed. Ontological location modeling adds reasoning flexibility and knowledge sharing capabilities.

Retrieving Assembly Part Design Using Case-Based Reasoning and Genetic Algorithms

2005 ◽  
Author(s):  
Guanghsu A. Chang ◽  
Cheng-Chung Su ◽  
John W. Priest
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithms ◽  
Nearest Neighbor ◽  
Similarity Measures ◽  
Experimental Results ◽  
Case Based Reasoning ◽  
Feature Weight ◽  
Similar Part ◽  
Case Base ◽  
Case Based

Artificial intelligence (AI) approaches have been successfully applied to many fields. Among the numerous AI approaches, Case-Based Reasoning (CBR) is an approach that mainly focuses on the reuse of knowledge and experience. However, little work is done on applications of CBR to improve assembly part design. Similarity measures and the weight of different features are crucial in determining the accuracy of retrieving cases from the case base. To develop the weight of part features and retrieve a similar part design, the research proposes using Genetic Algorithms (GAs) to learn the optimum feature weight and employing nearest-neighbor technique to measure the similarity of assembly part design. Early experimental results indicate that the similar part design is effectively retrieved by these similarity measures.

Methods of Software Quality Prediction With Similarity Measures

2017 ◽  
pp. 34-56
Keyword(s):  
Machine Learning ◽  
Software Quality ◽  
Similarity Measures ◽  
Fault Prediction ◽  
Estimation Accuracy ◽  
Case Based Reasoning ◽  
Quality Prediction ◽  
Software Fault Prediction ◽  
Software Quality Prediction ◽  
Case Based

To improve the software quality the number of errors or faults must be removed from the software. This chapter presents a study towards machine learning and software quality prediction as an expert system. The purpose of this chapter is to apply the machine learning approaches such as case-based reasoning to predict software quality. Five different similarity measures, namely, Euclidean, Canberra, Exponential, Clark and Manhattan are used for retrieving the matching cases from the knowledgebase. The use of different similarity measures to find the best method significantly increases the estimation accuracy and reliability. Based on the research findings in this book it can be concluded that applying similarity measures in case-based reasoning may be a viable technique for software fault prediction

R4 Model for Case-Based Reasoning and Its Application for Software Fault Prediction

2016 ◽  
Vol 8 (3) ◽  
pp. 19-38 ◽  
Author(s):  
Ekbal Rashid
Keyword(s):  
Software Quality ◽  
Similarity Measures ◽  
Fault Prediction ◽  
Maintenance Cost ◽  
Quality Level ◽  
Case Based Reasoning ◽  
Quality Prediction ◽  
Error Level ◽  
Software Fault Prediction ◽  
Case Based

Making R4 model effective and efficient I have introduced some new features, i.e., renovation of knowledgebase (KBS) and reducing the maintenance cost by removing the duplicate record from the KBS. Renovation of knowledgebase is the process of removing duplicate record stored in knowledgebase and adding world new problems along with world new solutions. This paper explores case-based reasoning and its applications for software quality improvement through early prediction of error patterns. It summarizes a variety of techniques for software quality prediction in the domain of software engineering. The system predicts the error level with respect to LOC and with respect to development time, and both affects the quality level. This paper also reviews four existing models of case-based reasoning (CBR). The paper presents a work in which I have expanded our previous work (Rashid et al., 2012). I have used different similarity measures to find the best method that increases reliability. The present work is also credited through introduction of some new terms like coefficient of efficiency, i.e., developer's ability.

Classification Algorithm of Case Retrieval Based on Granularity Calculation of Quotient Space

2020 ◽  
pp. 2150003
Author(s):  
Jiaxing Lu ◽  
Jiang Qing ◽  
Huang He ◽  
Zhang Zhengyong ◽  
Wang Rujing
Keyword(s):  
Quotient Space ◽  
Inner Product ◽  
Retrieval Algorithm ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Current Case ◽  
Quotient Spaces ◽  
Similarity Calculation ◽  
Key Steps ◽  
Case Based

Case retrieval is one of the key steps of case-based reasoning. The quality of case retrieval determines the effectiveness of the system. The common similarity calculation methods based on attributes include distance and inner product. Different similarity calculations have different influences on the effect of case retrieval. How to combine different similarity calculation results to get a more widely used and better retrieval algorithm is a hot issue in the current case-based reasoning research. In this paper, the granularity of quotient space is introduced into the similarity calculation based on attribute, and a case retrieval algorithm based on granularity synthesis theory is proposed. This method first uses similarity calculation of different attributes to get different results of case retrieval, and considers that these classification results constitute different quotient spaces, and then organizes these quotient spaces according to granularity synthesis theory to get the classification results of case retrieval. The experimental results verify the validity and correctness of this method and the application potential of granularity calculation of quotient space in case-based reasoning.

Sign in / Sign up

Export Citation Format

Share Document