Case Based Reasoning

The resources allocated for software quality assurance and improvement have not increased with the ever-increasing need for better software quality. A targeted software quality inspection can detect faulty modules and reduce the number of faults occurring during operations. We present a software fault prediction modeling approach with case-based reasoning (CBR), a part of the computational intelligence field focusing on automated reasoning processes. A CBR system functions as a software fault prediction model by quantifying, for a module under development, the expected number of faults based on similar modules that were previously developed. Such a system is composed of a similarity function, the number of nearest neighbor cases used for fault prediction, and a solution algorithm. The selection of a particular similarity function and solution algorithm may affect the performance accuracy of a CBR-based software fault prediction system. This paper presents an empirical study investigating the effects of using three different similarity functions and two different solution algorithms on the prediction accuracy of our CBR system. The influence of varying the number of nearest neighbor cases on the performance accuracy is also explored. Moreover, the benefits of using metric-selection procedures for our CBR system is also evaluated. Case studies of a large legacy telecommunications system are used for our analysis. It is observed that the CBR system using the Mahalanobis distance similarity function and the inverse distance weighted solution algorithm yielded the best fault prediction. In addition, the CBR models have better performance than models based on multiple linear regression.

Case-based reasoning (CBR) is a problem solving methodology that focuses on reusing lessons obtained from previous (possibly generalized) experiences towards solving new problems (Kolodner, 1993; Aamodt & Plaza, 1994; Watson, 1999; Bergmann, 2002). Originally conceived by cognitive scientists, since 1993 the CBR community has focused primarily on issues of interest to artificial intelligence researchers and practitioners. Some research topics of particular interest include case representation and indexing, solution retrieval and adaptation, learning (e.g., case acquisition), and integrating case-based approaches with others. Some motivating applications have included those related to customer support, recommender systems, knowledge management, diagnosis, the health sciences, and legal reasoning. Among these areas, most deployed CBR applications have been for the customer support niche of help-desk systems. These applications have the characteristic that problem descriptions are incrementally elicited from a user rather than available in complete form a priori. This category of CBR approaches is referred to as conversational (Aha et al., 2001) because they use a dialogue with the end user to identify the problem to be solved. Many case-based diagnosis and recommendation systems have used a conversational CBR (CCBR) methodology, and met with considerable success. In this presentation, I will introduce the CCBR methodology, describe its evolution, examine its various manifestations and their motivations, highlight recent work, and describe unsolved issues that could be the focus of enlightened dissertations.

We present a method for figure-ground segregation of moving objects from monocular video sequences. The approach is based on tracking extracted contour fragments, in contrast to traditional approaches which rely on feature points, regions, and unorganized edge elements. Specifically, a notion of similarity between pairs of curve fragments appearing in two adjacent frames is developed and used to find the curve correspondence. This similarity metric is elastic in nature and in addition takes into account both a novel notion of transitions in curve fragments across video frames and an epipolar constraint. This yields a performance rate of 85% correct correspondence on a manually labeled set of frame pairs. Color/intensity of the regions on either side of the curve is also used to reduce the ambiguity and improve efficiency of curve correspondence. The retrieved curve correspondence is then used to group curves in each frame into clusters based on the pairwise similarity of how they transform from one frame to the next. Results on video sequences of moving vehicles show that using curve fragments for tracking produces a richer segregation of figure from ground than current region or feature-based methods.

UML is widely accepted and used by industry for modelling and design of software systems. A novel method to automatically generate test cases based on UML state models is presented. In the present approach, the control and data flow logic available in the UML state diagram to generate test data are exploited. The state machine graph is traversed and the conditional predicates on every transition are selected. Then these conditional predicates are transformed and function minimisation technique is applied to generate test cases. The present test data generation scheme is fully automatic and the generated test cases satisfy transition path coverage criteria. The generated test cases can be used to test class as well as cluster-level state-dependent behaviours.

SQL Injection attacks on web applications have become one of the most important information security concerns over the past few years. This paper presents a hybrid approach based on the Adaptive Intelligent Intrusion Detector Agent (AIIDA-SQL) for the detection of those attacks. The

The paper provides an OWL ontology for legal cases with an instantiation of the legal case Popov v. Hayashi. The ontology makes explicit the conceptual knowledge of the legal case domain, supports reasoning about the domain, and can be used to annotate the text of cases, which in turn can be used to populate the ontology. A populated ontology is a case base which can be used for information retrieval, information extraction, and case based reasoning. The ontology contains not only elements of indexing the case (e.g. the parties, jurisdiction, and date), but as well elements used to reason to a decision such as argument schemes and the components input to the schemes. We use the Protégé ontology editor and knowledge acquisition system, current guidelines for ontology development, and tools for visual and linguistic presentation of the ontology. 1

Mémoire proposes a general framework for reasoning from cases in biology and medicine. Part of this project is to propose a memory organization capable of handling large cases and case bases as occur in biomedical domains. This article presents the essential principles for an efficient memory organization based on pertinent work in information retrieval. Information retrieval systems have been able to scale up to terabyte of data taking advantage of large databases research to build Internet search engines. They search for pertinent documents to answer a query using term-based ranking and/or global ranking schemes. Similarly, CBR systems search for pertinent cases using a scoring function for ranking the cases. Mémoire proposes a memory organization based on inverted indexes, which may be powered by databases to search and rank efficiently through large case bases. It can be seen as a first step toward largescale CBR systems, and in addition provides a framework for tight cooperation between CBR and IR.

Multi document analysis has been a field of interest for decades and is still being actively researched until today. One example of such analysis could be for the task of multi document summarization which is meant to represent the concise description of the original documents. In this paper, we will focus on some special properties that multi document articles hold, specifically news articles. Information across news articles reporting on the same story are often related. Cross-document structure theory (CST) gives several relationships between pairs of sentences from different documents. Among them, we focus on four relations namely "Identity", "Overlap", "Subsumption", and "Description". Our aim is to automatically identify these CST relationships. We applied three machine learning techniques, i.e. SVM, neural network and our proposed case-based reasoning (CBR) model. Comparison between these techniques shows that the proposed CBR model yields better results.

Most recommender systems present recommended products in lists to the user. By doing so, much information is lost about the mutual similarity between recommended products. We propose a graphical shopping interface, which represents the mutual similarities of the recommended products in a two dimensional space, where similar products are located close to each other and dissimilar products far apart. The graphical shopping interface can be used to navigate through the complete product space in a number of steps. We show a prototype application of the system to MP3-players.

Thalassaemia is a genetic blood disorder where the blood cells are unable to carry sufficient oxygen supply for the organs. The Thalassaemia has a distribution concomitant with areas where P. Falciparum malaria is common. The alpha Thalassaemia is concentrated in Southeast ...

Case-based reasoning (CBR) is one of the most popular prediction techniques in medical domains because it is easy to apply, has no possibility of overfitting, and provides a good explanation for the output. However, it has a critical limitation -its prediction performance is generally lower than other AI techniques like artificial neural networks (ANN). In order to obtain accurate results from CBR, effective retrieval and matching of useful prior cases for the problem is essential, but it is still a controversial issue to design a good matching and retrieval mechanism for CBR systems. In this study, we propose a novel approach to enhance the prediction performance of CBR. Our suggestion is the simultaneous optimization of feature weights, instance selection, and the number of neighbors that combine using genetic algorithms (GA). Our model improves the prediction performance in three ways -(1) measuring similarity between cases more accurately by considering relative importance of each feature, (2) eliminating useless or erroneous reference cases, and (3) combining several similar cases represent significant patterns. To validate the usefulness of our model, this study applied it to a realworld case for evaluating cytological features derived directly from a digital scan of breast fine needle aspirate (FNA) slides. Experimental results showed that the prediction accuracy of conventional CBR may be improved significantly by using our model. We also found that our proposed model outperformed all the other optimized models for CBR using GA.

Nowadays, the complexity involved in computer network fault management demands a great amount of information about the involved technologies and their associated problems. Besides, Trouble Ticket Systems have been used to store the occurred problems, actuating as an historical memory of the network. Thus, a correct approach to consolidate the network historic memory is the development of an expert system that takes in account the knowledge accumulated in the Trouble Ticket Systems to propose solutions for an average problem. This work presents a system that uses Case-Based Reasoning paradigm applied to a Trouble Ticket System to suggest solutions for a new problem occurred. This system aims to aid diagnosis and resolution stages of network management problems. Typical problems of this domain, the proposed solution and results reached with the developed prototype are described. DUMBO System [10] consists in a case-based reasoning system that was developed upon the architecture and technology of traditional TTS. A trouble ticket can be easily conceived as a case and the trouble ticket base conceived as the case base of the system. It remains, so, to add to the architecture of such systems the reasoning processes, and to add about the additional information in traditional tickets, so that, these processes can be performed. These topics will be commented in the next sections.

Case-based reasoning (CBR) is a paradigm for combining problem solving and learning that has become one of the most successful applied subfields of AI in recent years. Now that CBR has become a mature and established technology two necessities have become critical: the availability of tools to build CBR systems, and the accumulated practical experience of applying CBR techniques to real-world problems. In this paper we are presenting jCOLIBRI, an object-oriented framework in Java for building CBR systems, that greatly benefits from the reuse of previously developed CBR systems.

As with the growing degree of office automation and diffuse use of electronic media, such as e-mails, written business communication is becoming a key element to promote synergies, relationships and disseminating information about products and services. Task recognition and the definition of strategies and suitable vocabularies are some of the activities that office workers deal with each time a communicative intent has to be effectively transferred and understood by a given addressee. This paper introduces a web-based intelligent training system based on the constructivism theory and self-directed learning paradigms for assisting company workers in the drafting business letters-writing task. A case-based engine suggests ad hoc rhetorical letters that users have the chance to adapt to their particular contexts and save them into user-defined case libraries.

Resolving domain incompatibility among independently developed databases often involves uncertain information. DeMichiel (1989) showed that uncertain information can be generated by the mapping of conflicting attributes to a common domain, based on some domain knowledge. The authors show that uncertain information can also arise when the database integration process requires information not directly represented in the component databases, but can be obtained through some summary of data. They therefore propose ...

Health is essential for everyone. Health can’t be bought by anything. Health is important no matter what country we are from or what race we
are, or what age or gender we are. some important details like the patients allergy and medical history will be overlooked by the doctors when they
diagnosing. The procedure for diagnosing also consume a big amount of time although the patients comes with a minor problem that they can settle it by
themselves if they have the right help. This paper discusses about the Web Based Online Medical Diagnosis system. This system is split into three main parts
that comprise of Patient’s Registration and Administration, Diagnosis and Treatment and Health Monitor and Tips.

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A design pattern is a successful solution to recurring problems. It is a powerful tool to improve design quality and to reduce the time and cost of design. One of the major challenges confronting developers while using of design pattern is finding a suitable selection for them, thus it is considered as a challenging task for developers with less experience in the area of the design pattern. Therefore, the developers need a pattern recommendation to assist them by suggesting suitable design patterns. This paper presents the architecture Design Pattern Selection based on a Multi-Agent System (DPSA). The DPSA aims to obtain the appropriate recommendation to reduce development efforts, facilitate and assist the developers in selecting suitable patterns for their problems. DPSA's agents operate to support four different types of recommendations: single pattern, implement the pattern, pattern sequences, and recommending practical.

Case-Based Reasoning (CBR) is the process of solving a given problem based on the knowledge gained from solving precedents. It is an effective technique in the area of customer services or helpdesks. That is, a CBR system is used to solve most of the commonly occurring customer problems. While the implementation techniques may vary, most CBR systems include the following five steps: case representation and storage, precedent matching and retrieval, adaptation of the retrieved solution, validation of the solution, and finally, casebase update to include the information gained from the new problem. This paper dletails the various implementation techniques for these five steps, while focusing on a particular helpdesk system, namely SmartUSA, developed for the Union Camp Corporation. This system solves a customer's problem by filtering the problem description through an alias table to generate a brief description and then matching the brief description with the ca.ses in the database. It has proved to be an effective and user-friendly system that has successfully handled different descriptions of the same problem and allowed for the casebase to be built in free-format (plain) text. This system has significantly reduced the workload and the response time in the customer services department of the Union Camp Corporation.

Context awareness was introduced in several fields in routine human activities. Healthcare systems are the most important ones among context-aware applications. The existence and awareness of the context made mobile device users conscious of physical environments or situations. Smart devices, armed with multiple sensors, can sense and react based on their environment, and become context-aware agents. Smart devices are ideal assistants that provide accurate solutions for the critical aspects of healthcare, and medical staff works as well as the closer up to date state
of the patient. In addition, hospital's staff members can communicate with the patients reliably and suitably by using smart devices offers. But the applications of healthcare have lack of standardization in handling the context and the perceived sensors data. To perceive the context, we can rely on sensors, which may be physical or virtual. More generally, our research will concern on contextual applications of healthcare that aroused increasingly important interest. To improve the user experience, the advantage of contextual data is aimed to take by context-aware
applications. A useful tool to reason about context is proved by using case-based reasoning (CBR) and fuzzy techniques to deal with lack of standardization in handling the context and uncertainty of data. On the other side, it could be interesting to analyse how context reasoning relates to CBR. To assess how CBR can be integrated with contextual information is the primary concern of our research study. So, in this paper, we concrete on the mobile application of healthcare that deals with contextual data by using CBR techniques to analyse the context data. It gives a perfect situation in a right way or prediction for providing accurate solutions for the critical aspect of healthcare and medical staff work. It can overcome the problem of the standardization for dealing with context data.

ABSTRACT Designers often use previous knowledge during design activities. Case-Based Reasoning (CBR) systems have been used to help designers deal with a large repertoire of past design cases in many domains, like architecture and software. However, we still have little work that addresses Human-Computer Interaction (HCI) design in CBR systems. In general, they focus on specific HCI design artifacts, considering just a few aspects of HCI solutions. In this paper, we review related works to propose an enlarged conceptual model for HCI design cases in CBR systems. The model is flexible and extensible to accommodate different design artifacts, supporting different design processes and cultures. We have evaluated our conceptual model with a qualitative research, in which participants were asked to index and retrieve cases during an HCI design activity, referring to characteristics of context of use, domain, user, user goal, interaction, user interface, and system. The results indicate that CBR systems for HCI design should consider a wide view of the HCI problem and solution spaces, and the proposed conceptual model has great potential to support designers in dealing with a large repertoire of HCI design cases in CBR systems.

Over the last two decades, there has been significant activity in the soft computing arena with focus on various computer paradigms such as neural networks, genetic algorithms, and fuzzy logic to more efficiently solve complex engineering problems. Further work concentrated on integrating two or more of these paradigms and led to what is known as "hybrid systems". The power of the hybrid system relies on the fact that technologies are intended to complement each other and exploit their individual strengths to enhance solution generation.

Information technology and the Internet have been major drivers for changes in all aspects of business processes and activities. They have brought major changes to the financial statements audit environment as well, which in turn has required modifications in audit procedures. There exist certain difficulties, however, with current audit procedures especially for the assessment of the level of control risk. This assessment is primarily based on the auditors' professional judgment and experiences, not on objective rules or criteria.

The effective estimation of costs is crucial to the success of construction projects. Cost estimates are used to evaluate, approve and/or fund projects. Organizations use some form of classification system to identify the various types of estimates that may be prepared during the lifecycle of a project. This research presents a parametric-cost model for pump station projects. Fourteen factors have been identified as important to the influence of the cost of pump station projects. A data set that consists of forty-four pump station projects (fifteen water and twenty-nine waste water) are collected to build a Case-Based Reasoning (CBR) library and to test its performance. The results obtained from the CBR tool are processed and adopted to improve the accuracy of the results. A numerical example is presented to demonstrate the development of the effectiveness of the tool.

Current computer-chess programs achieve outstanding results in chess playing. However, there is a deficiency of evaluative comments on chess positions. In this paper, we propose a case-based model that supplies a comprehensive positional analysis for any given position. This analysis contains evaluative comments for the most significant basic features found in the position and a general evaluation for the entire position. The analysis of the entire position is presented by an appropriate Multiple eXplanation Pattern (MXP), while the analysis of each chosen feature is presented by a suitable eXplanation Pattern (XP). The proposed analysis can improve weak and intermediate players' play in general and their understanding, evaluating and planning abilities in particular. This model is part of an intelligent educational chess system which is under development. At present, our model deals only with a static evaluation of chess positions; addition of searching and playing modules remains for future work.

Este texto digital es de DOMINIO PÚBLICO en Ecuador por cumplirse más de 50 años de la muerte de su autor. Sin embargo no todas las leyes de Copyright son iguales en los diferentes países del mundo. Infórmese de la situación de su país antes de la distribución pública de este texto.

We present a computational model of case-based visual problem solving. The Galatea model and the two experimental participants modeled in it show that 1) visual knowledge is sufficient for transfer of some problemsolving procedures, 2) visual knowledge facilitates transfer even when non-visual knowledge might be available, and 3) the successful transfer of strongly-ordered procedures in which new objects are created requires the reasoner to generate intermediate knowledge states and mappings between the intermediate knowledge states of the source and target cases. We describe Galatea, the two models created with it, and related work.

Relational reasoning, which has been defined as the ability to discern meaningful patterns within any informational stream, is a foundational cognitive ability associated with education, including in scientific domains. This study entailed the analysis of instructional conversations in which an attending clinical neurologist and his team of residents made diagnostic and therapeutic decisions about actual patients in a hospital setting. The primary goal was to investigate the role of 4 manifestations of relational reasoning (i.e., analogy, anomaly, antinomy, and antithesis) in medical education and diagnostic and therapeutic decision making. Results indicated that the degree to which members of the medical team used the 4 forms of relational reasoning depended on their role and expertise, as well as the time point in the problem-solving process. Specific reasoning patterns that emerged in the discourse and a prototypical model of the reasoning process are described and implications for research and practice are considered.

This paper outlines the development of a knowledge-based system (KBS) which integrates environmental factors into the supplier selection process. The system employs both case-based reasoning (CBR) and decision support components including multi-attribute analysis (MAA). Traditionally, when evaluating supplier performance, companies have considered factors such as price, quality, flexibility etc. However, with environmental pressures increasing, many companies have begun to consider environmental issues and the measurement of their suppliers' environmental performance. The framework presented in this paper was developed from an analysis of environmental management practices in a number of companies along with a thorough literature survey. The paper then outlines how the most important parts of the framework were computerised using KBS techniques. An evaluation of the system implemented in a multinational company is presented along with proposals for future system enhancements. q

The oil well drilling process is the selected representative of a challenging industrial process. The drilling process is becoming more complex as oil fields mature and technology evolves. At the same time, the amount of information is increasing in volume and frequency. Although technology is advancing, failures occur at almost the same rate as before, leading to loss of valuable time. Whenever the process is failing, or running smoothly, valuable experience is gained. To take advantage of established and continually growing new experience a formalized methodology, knowledge intensive case-based reasoning, was applied for capturing of drilling process experience and for reusing it. Experience was collected from different information sources. Structured cases were used to describe failure episodes; its circumstances and how the failure was repaired. A general domain knowledge model supports the case-based reasoning process. It was demonstrated how the system was able to recommend how to solve problems when they arise, while at the same time bridging the gap between new and experienced personnel. Method performance was tested on 62 selected field cases. The system also identified the failure causes of problems and could thereby suggest more effective repair actions.

One of the major assumptions in case-based reasoning is that similar experiences can guide future reasoning, problem solving and learning. This assumption shows the importance of the method used for choosing the most suitable case, especially when dealing with the class of problems in which risk, is relevant concept to the case retrieval process. This paper argues that traditional similarity assessment methods are not sufficient to obtain the best case; an additional step with new information must be performed necessary, after applying similarity measures in the retrieval stage. When a case is recovered from the case base, one must take into account not only the specific value of the attribute but also whether the case solution is suitable for solving the problem, depending on the risk produced in the final decision. We introduce this risk, as new information through a new concept called risk information that is entirely different from the weight of the attributes. Our article presents this concept locally and measures it for each attribute independently.

The purpose of this research is to propose a procurement system across other disciplines and retrieved information with relevant parties so as to have a better co-ordination between supply and demand sides. This paper demonstrates how to analyze the data with an agent-based procurement system (APS) to re-engineer and improve the existing procurement process. The intelligence agents take the responsibility of searching the potential suppliers, negotiation with the short-listed suppliers and evaluating the performance of suppliers based on the selection criteria with mathematical model. Manufacturing firms and trading companies spend more than half of their sales dollar in the purchase of raw material and components. Efficient data collection with high accuracy is one of the key success factors to generate quality procurement which is to purchasing right material at right quality from right suppliers. In general, the enterprises spend a significant amount of resources on data collection and storage, but too little on facilitating data analysis and sharing. To validate the feasibility of the approach, a case study on a manufacturing small and medium-sized enterprise (SME) has been conducted. APS supports the data and information analyzing technique to facilitate the decision making such that the agent can enhance the negotiation and suppler evaluation efficiency by saving time and cost.