A use of case-based reasoning technique in building expert systems

Matra Marconi Space France and Aramiihs (Action de Recherche et Application Matra Irit en Interaction Homme Système) laboratory have used and evaluated Case Based Reasoning (CBR) techniques in two projects:

• - The first project is about the development of a system dedicated to help satellites AIT/AIV (Assembly Integration and Test/Validation) test engineers to cope with incidents occurring during test activities. The project is funded by the EGSE System Section of ESTEC (European Space Research and Technology Centre.).

• - The second project is related to the building of a knowledge-based system for diagnosis assistance in AIT/AIV activities of Ariane4 Vehicle Equipment Bay (VEB). The project is financed by internal funding of MMS-F.

In the two projects, CBR technique is neither used the same way nor with the same purpose.

In the first project, CBR technique is used to find out or suggest the cause of an anomaly when an incident appears. Confronted with the occurrence of an incident, the system will refer to its characteristics (test context, symptoms…) that are considered as relevant to retrieve previous similar incidents.

In the second project, CBR technique is combined with Rule Based Reasoning and Model Based Reasoning ones to form the reasoning core of a Hybrid Knowledge Based System. When an incident occurs, the system proposes to test engineers a diagnosis approach based on the combination of different knowledge (coded into rule, cases and models).

Aramiihs is a research unit where engineers from MMS and researchers from the IRIT (Institut de Recherche en Informatique de Toulouse) CNRS (Centre National de la Recherche Scientifique) collaborate on problems concerning new types of man-system interaction.     

Developing an employee turnover risk evaluation model using case-based reasoning

All enterprises are concerned with employee turnover risk due to the significant impact on their effectiveness and competitiveness. Evaluation of the risk is a frequent topic in the literature. However, the majority of past work has not incorporated the advancement of modern information technology, particularly in the era of Internet of Things (IoT). In this paper, we propose to use an artificial intelligence method, case-based reasoning (CBR), to develop a multi-level employee turnover risk evaluation model. The proposed model adopts multiple CBR techniques including case representation, organization and management, and retrieval and matching to evaluate employee turnover risk. Specifically, we employ an object-oriented method in case knowledge expressing, utilize relational database in case organization and management, and follow a tree-hash algorithm to retrieve the best cases. Both theoretical and practical implications of the proposed model are discussed.     

Learning-based scheduling of flexible manufacturing systems using case-based reasoning

A common method of dynamically scheduling jobs in Flexible Manufacturing Systems (FMSs) is to employ dispatching rules. However, the problem associated with this method is that the performance of the rules depends on the state of the system, but there is no rule that is superior to all the others for all the possible states the system might be in. It would therefore be highly desirable to employ the most suitable rule for each particular situation. To achieve this, this paper presents a scheduling approach that uses Case-Based Reasoning (CBR), which analyzes the system's previous performance and acquires "scheduling knowledge," which determines the most suitable dispatching rule at each particular moment in time. Simulation results indicate that the proposed approach produces significant performance improvements over existing dispatching rules.     

Multitemporal SAR images for monitoring cultivation systems using case-based reasoning

This paper demonstrates that multitemporal satellite SAR images are most suitable for monitoring the rapid changes of cultivation systems in a subtropical region. A new method is proposed by applying case-based reasoning (CBR) techniques to the classification of SAR images. Stratified sampling is carried out to collect the cases so that the variations of backscatters within a class can be appropriately captured. The use of discrete cases can conveniently represent the internal changes of a class under complicated situations, such as spatial changes in soil conditions and terrain features. These spatial variations are difficult to represent by using rules or mathematical equations. The proposed method has better classification performance than supervised classification methods in the study area. The case library is reusable for time-independent classification when the SAR images are acquired at the same time of the crop growth cycles for different years. The proposed method has been tested in the Pearl River Delta in South China.     

Scenario-based knowledge representation in case-based reasoning systems

A scenario-based representation model for cases in the domain of managerial decision-making is proposed. The scenarios in narrative texts are converted to scenario units of knowledge organization. The elements and structure of the scenario unit are defined. The scenario units can be linked together or coupled with others. Compared with traditional case representation methods based on database tables or frames, the proposed model is able to represent knowledge in the domain of managerial decision-making at a much deeper level and provide much more support for case-based systems employed in business decision-making.     

Improving user experience with case-based reasoning systems using text mining and Web 2.0

Many CBR systems have been developed in the past. However, currently many CBR systems are facing a sustainability issue such as outdated cases and stagnant case growth. Some CBR systems have fallen into disuse due to the lack of new cases, case update, user participation and user engagement. To encourage the use of CBR systems and give users better experience, CBR system developers need to come up with new ways to add new features and values to the CBR systems. The author proposes a framework to use text mining and Web 2.0 technologies to improve and enhance CBR systems for providing better user experience. Two case studies were conducted to evaluate the usefulness of text mining techniques and Web 2.0 technologies for enhancing a large scale CBR system. The results suggest that text mining and Web 2.0 are promising ways to bring additional values to CBR and they should be incorporated into the CBR design and development process for the benefit of CBR users.     

An empirical study of modeling self-management capabilities in autonomic systems using case-based reasoning

Autonomic systems promise to inject self-managing capabilities in software systems. The major objectives of autonomic computing are to minimize human intervention and to enable a seamless self-adaptive behavior in the software systems. To achieve self-managing behavior, various methods have been exploited in past. Case-based reasoning (CBR) is a problem solving paradigm of artificial intelligence which exploits past experience, stored in the form of problem–solution pairs. We have applied CBR based modeling approach to achieve autonomicity in software systems. The proposed algorithms have been described and CBR implementation on externalization and internalization architectures of autonomic systems using two case studies RUBiS and Autonomic Forest Fire Application (AFFA) have been shown. The study highlights the effect of 10 different similarity measures, the role of adaptation and the effect of changing nearest neighborhood cardinality for a CBR solution cycle in autonomic managers. The results presented in this paper show that the proposed CBR based autonomic model exhibits 90–98% accuracy in diagnosing the problem and planning the solution.     

An empirical study of modeling self-management capabilities in autonomic systems using case-based reasoning

Autonomic systems promise to inject self-managing capabilities in software systems. The major objectives of autonomic computing are to minimize human intervention and to enable a seamless self-adaptive behavior in the software systems. To achieve self-managing behavior, various methods have been exploited in past. Case-based reasoning (CBR) is a problem solving paradigm of artificial intelligence which exploits past experience, stored in the form of problem–solution pairs. We have applied CBR based modeling approach to achieve autonomicity in software systems. The proposed algorithms have been described and CBR implementation on externalization and internalization architectures of autonomic systems using two case studies RUBiS and Autonomic Forest Fire Application (AFFA) have been shown. The study highlights the effect of 10 different similarity measures, the role of adaptation and the effect of changing nearest neighborhood cardinality for a CBR solution cycle in autonomic managers. The results presented in this paper show that the proposed CBR based autonomic model exhibits 90–98% accuracy in diagnosing the problem and planning the solution.     

Decision-theoretic case-based reasoning

We describe a decision-theoretic methodology for case-based reasoning in diagnosis and troubleshooting applications. The system utilizes a special-structure Bayesian network to represent diagnostic cases, with nodes representing issues, causes, and symptoms. Dirichlet distributions are assessed at knowledge acquisition time to indicate the strength of relationships between variables. During a diagnosis session, a relevant subnetwork is extracted from a Bayesian-network database that describes a very large number of diagnostic interactions and cases. The constructed network is used to make recommendations regarding possible repairs and additional observations, based on an estimate of expected repair costs. As cases are resolved, observations of issues, causes, symptoms, and the success of repairs are recorded. New variables are added to the database, and the probabilities associated with variables already in the database are updated. In this way, the inferential behavior of system adjusts to the characteristics of the target population of users. We show how these elements work together in a cycle of troubleshooting tasks, and describe some results from a pilot system implementation and deployment     

Continuous case-based reasoning

Case-based reasoning systems have traditionally been used to perform high-level reasoning in problem domains that can be adequately described using discrete, symbolic representations. However, many real-world problem domains, such as autonomous robotic navigation, are better characterized using continuous representations. Such problem domains also require continuous performance, such as on-line sensorimotor interaction with the environment, and continuous adaptation and learning during the performance task. This article introduces a new method for continuous case-based reasoning, and discusses its application to the dynamic selection, modification, and acquisition of robot behaviors in an autonomous navigation system, SINS (self-improving navigation system). The computer program and the underlying method are systematically evaluated through statistical analysis of results from several empirical studies. The article concludes with a general discussion of case-based reasoning issues addressed by this research.     

Adaptive case-based reasoning using retention and forgetting strategies

Case-based reasoning systems need to maintain their case base in order to avoid performance degradation. Degradation mainly results from memory swamping or exposure to harmful experiences and so, it becomes vital to keep a compact, competent case base. This paper proposes an adaptive case-based reasoning model that develops the case base during the reasoning cycle by adding and removing cases. The rationale behind this approach is that a case base should develop over time in the same way that a human being evolves her overall knowledge: by incorporating new useful experiences and forgetting invaluable ones. Accordingly, our adaptive case-based reasoning model evolves the case base by using a measure of “case goodness” in different retention and forgetting strategies. This paper presents empirical studies of how the combination of this new goodness measure and our adaptive model improves three different performance measures: classification accuracy, efficiency and case base size.     

Block assembly planning in shipbuilding using case-based reasoning

A process planning system using case-based reasoning (CBR) is developed for block assembly in shipbuilding. A block assembly planning problem is modeled as a constraint satisfaction problem where the precedence relations between operations are considered constraints. In order to find similar cases, we propose two similarity coefficients for finding similar cases and for finding similar relations. Due to the limited number of operation types, the process planning system first matches the parts of the problem and those of the case-based on their roles in the assembly, and then it matches the relations related to the matched part–pairs. The parts involved in more operations are considered first. The process planning system is applied to simple examples for verification and comparison. An interface system is also developed for extracting information from CAD model, for preparing data for process planning, and for visually verifying the assembly sequence.     

Predicting business failure using forward ranking-order case-based reasoning

With the rapid development of business computing for Chinese listed companies, it is focused on to use case-based reasoning (CBR) in business failure prediction (BFP). Ranking-order case-based reasoning (RCBR) uses ranking-order information among cases to calculate similarity in the framework of k-nearest neighbor. RCBR is sensitive to the choice of features, meaning that optimal features can help it produce better performance. In this research, we attempt to use wrapper approach to find the optimal feature subset for RCBR in BFP. Forward feature selection method and RCBR are combined to construct a new method, namely forward RCBR (FRCBR). The combination is implemented by combining forward feature selection with RCBR as a wrapper module. Hold out method is used to assessing the performance of the classifier. Empirical data were collected from Chinese listed companies in the Shenzhen Stock Exchange and Shanghai Stock Exchange. We employed the standalone RCBR, the classical CBR with Euclidean metric as its heart, the inductive CBR, the two statistical methods of logistic regression and multivariate discriminate analysis (MDA), and support vector machines to make comparisons. For comparative methods, stepwise MDA was employed to select optimal feature subset. Empirical results indicated that FRCBR can produce dominating performance in short-term BFP of Chinese listed companies.     

From case-based reasoning to traces-based reasoning

CBR is an original AI paradigm based on the adaptation of solutions of past problems in order to solve new similar problems. Hence, a case is a problem with its solution and cases are stored in a case library. The reasoning process follows a cycle that facilitates “learning” from new solved cases. This approach can be also viewed as a lazy learning method when applied for task classification. CBR is applied for various tasks as design, planning, diagnosis, information retrieval, etc. The paper is the occasion to go a step further in reusing past unstructured experience, by considering traces of computer use as experience knowledge containers for situation based problem solving.     

A connectionist approach for similarity assessment in case-based reasoning systems

Case-Based Reasoning (CBR) systems support ill-structured decision making. In ill-structured decision environments, decision makers (DMs) differ in their problem solving approaches. As a result, CBR systems would be more useful if they were able to adapt to the idiosyncrasies of individual decision makers. Existing implementations of CBR systems have been mainly symbolic, and symbolic CBR systems are unable to adapt to the preferences of decision makers (i.e., they are static). Retrieval of appropriate previous cases is critical to the success of a CBR system. Widely used symbolic retrieval functions, such as nearest-neighbor matching, assume independence of attributes and require specification of their importance for matching. To ameliorate these deficiencies connectionist systems have been proposed. However, these systems are limited in their ability to adapt and grow. To overcome this limitation, we propose a distributed connectionist-symbolic architecture that adapts to the preferences of a decision maker and that, additionally, ameliorates the limitations of symbolic matching. The proposed architecture uses a supervised learning technique to acquire the matching knowledge. The architecture allows the growth of a case base without the involvement of a knowledge engineer. Empirical investigation of the proposed architecture in an ill-structured diagnostic decision environment demonstrated a superior retrieval performance when compared to the nearest-neighbor matching function.     

Improving the tutoring of software design using case-based reasoning

Judging by results, the methods undertaken to teach software development to large classes of students are flawed; too many students are failing to grasp any real understanding of programming and software design. To address this problem the University of Wales, Aberystwyth has developed VorteX, an interactive collaborative design tool that captures the design processes of novice students, provides a diagnosis system capable of interpreting the students’ work, and advises on their design process.This paper provides an overview of VorteX, its capabilities and use, and explains how the case-based system identifies redundancies in the storage of student designs and reduces data volume. The paper describes how equivalence maps merge similar classes to reduce the design structure possibilities, how snippets eliminate the replication of components and how abstract snippets represent the design intent of students in a minimalist form. Finally it concludes with comments on the student experience of the VorteX case-based reasoning assistant.     

Hybrid genetic algorithms and case-based reasoning systems for customer classification

Abstract: Because of its convenience and strength in complex problem solving, case-based reasoning (CBR) has been widely used in various areas. One of these areas is customer classification, which classifies customers into either purchasing or non-purchasing groups. Nonetheless, compared to other machine learning techniques, CBR has been criticized because of its low prediction accuracy. Generally, in order to obtain successful results from CBR, effective retrieval of useful prior cases for the given problem is essential. However, designing a good matching and retrieval mechanism for CBR systems is still a controversial research issue. Most previous studies have tried to optimize the weights of the features or the selection process of appropriate instances. But these approaches have been performed independently until now. Simultaneous optimization of these components may lead to better performance than naive models. In particular, there have been few attempts to simultaneously optimize the weights of the features and the selection of instances for CBR. Here we suggest a simultaneous optimization model of these components using a genetic algorithm. To validate the usefulness of our approach, we apply it to two real-world cases for customer classification. Experimental results show that simultaneously optimized CBR may improve the classification accuracy and outperform various optimized models of CBR as well as other classification models including logistic regression, multiple discriminant analysis, artificial neural networks and support vector machines.