Explanation for case-based reasoning via abstract argumentation

Case-based reasoning (CBR) is extensively used in AI in support of several applications, to assess a new situation (or case) by recollecting past situations (or cases) and employing the ones most similar to the new situation to give the assessment. In this paper we study properties of a recently proposed method for CBR, based on instantiated Abstract Argumentation and referred to as AA-CBR, for problems where cases are represented by abstract factors and (positive or negative) outcomes, and an outcome for a new case, represented by abstract factors, needs to be established. In addition, we study properties of explanations in AA-CBR and define a new notion of lean explanations that utilize solely relevant cases. Both forms of explanations can be seen as dialogical processes between a proponent and an opponent, with the burden of proof falling on the proponent

Analysing similarity assessment in feature-vector case representations

Case-Based Reasoning (CBR) is a good technique to solve new problems based in previous experience. Main assumption in CBR relies in the hypothesis that similar problems should have similar solutions. CBR systems retrieve the most similar cases or experiences among those stored in the Case Base. Then, previous solutions given to these most similar past-solved cases can be adapted to fit new solutions for new cases or problems in a particular domain, instead of derive them from scratch. Thus, similarity measures are key elements in obtaining reliable similar cases, which will be used to derive solutions for new cases. This paper describes a comparative analysis of several commonly used similarity measures, including a measure previously developed by the authors, and a study on its performance in the CBR retrieval step for feature-vector case representations. The testing has been done using six-teen data sets from the UCI Machine Learning Database Repository, plus two complex environmental databases.Postprint (published version

The Effectiveness of Case-Based Reasoning: An Application in Sales Promotions

This paper deals with Case-based Reasoning (CBR) as a support technology for sales promotion (SP) decisions. CBR-systems try to mimic analogical reasoning, a form of human reasoning that is likely to occur in weakly-structured problem solving, such as the design of sales promotions. In an empirical study, we find evidence that use of the CBR-system improves the quality of SP-campaign proposals. In terms of the creativity of the proposals, decision-makers who think highly divergent (i.e., who tend to generate many, and diverse ideas in response to a problem) benefit most from prolonged system usage. Creativity, in turn, is positively related to the (practical) usability of a proposal. These results suggest that the CBR-system is most effective when it is used as an idea-generation tool that reinforces the strength of divergent (creative) thinkers. A convergent thinking style, in which case the CBR-system has a compensating role, even has a negative impact on CBR-system usage. Increasing the decision-maker's personal belief in the usefulness of the system, e.g., by training or education, may help to alleviate this reluctance to use the CBR-system.marketing management support systems;sales promotions;case-based reasoning;weakly-structured decision making

A methodology to conceive a case based system of industrial diagnosis.

International audienceThe objective of this paper is to address the diagnosis knowledge-oriented system in terms of artificial intelligence, particular by the Case-Based Reasoning (CBR) approach. Indeed, the use of CBR, which is an approach to problem solving and learning, in diagnosis goes back to a long time with the appearance of diagnostic support systems based on CBR. A diagnostic system by CBR implements an expertise-base composed of past experiences through which the origins of failure and the maintenance strategy are given according to a description of a specific situation of diagnostic. A study is made on the different diagnostic systems based on CBR. This study showed that there was no common methodology for building a CBR system. This design depends primarily on the case representation and knowledge models of the domain application. Consequently, this paper proposes a general design approach of a diagnostic system based on the CBR approach

A case-based reasoning approach for low volume, high added value electronics

This paper will report on the application of the Case-Based Reasoning (CBR) approach [1] to develop a defect prediction system to support the development of new printed circuit assembly (PCA) products. Using a CBR system, past PCA design specifications and manufacturing experiences including defect and yield results can be effectively stored and reapplied for future problem solving. For example, the CBR can then be used at design stage to amend designs or define process options to optimise the product yield and service reliability. A case study using a case-base provided by a PCA manufacturer is presented

Case Based Reasoning Method untuk Sistem Pakar Diagnosa Penyakit Sapi

This study discusses about the Application of Case Based Reasoning (CBR) Method for Expert Systems in Diagnosing Cattle Disease. Beginning with data collection by consulting experts in the Department of Agriculture in Animal Health, Pohuwato Regency. The data obtained in the form of data names of disease and symptom data. The data is obtained based on the steps of the CBR method calculation in order to obtain the results of the diagnosis and the solution provided for handling the disease. Researcher have analyzed and create program listings to build a system that will be used by farmers. Based on CBR calculations Scours case which has the lowest weight is 0.09 while the highest weight is owned by the Pink Eye case 1. In this process provides a solution to the similarity of the case weight from the old case to the new higher case. In the case of Pink Eye having a higher weight and positive exposure to pink eye disease, the solution given is the provision of anti-allergic, anti-biotic and vitamin. Based on the results obtained, it can be concluded that the application of the CBR method is good for using cattle disease and is very helpful for farmers in dealing with cattle disease.Â

AIDeM: Agent-Based Intrusion Detection Mechanism

The availability of services can be comprimised if a service request sent to the web services server hides some form of attack within its contents. This article presents AIDeM (An Agent-Based Intrusion Detection Mechanism), an adaptive solution for dealing with DoS attacks in Web service environments. The solution proposes a two phased mechanism in which each phase incorporates a special type of CBR-BDI agent that functions as a classifier. In the first phase, a case-based reasoning (CBR) engine utilizes a Naïves Bayes strategy to carry out an initial filter, and in the second phase, a CBR engine incorporates a neural network to complete the classification mechanism. AIDeM has been applied within the FUSION@ architecture to improve its current security mechanism. A prototype of the architecture was developed and applied to a case study. The results obtained are presented in this study.The availability of services can be comprimised if a service request sent to the web services server hides some form of attack within its contents. This article presents AIDeM (An Agent-Based Intrusion Detection Mechanism), an adaptive solution for dealing with DoS attacks in Web service environments. The solution proposes a two phased mechanism in which each phase incorporates a special type of CBR-BDI agent that functions as a classifier. In the first phase, a case-based reasoning (CBR) engine utilizes a Naïves Bayes strategy to carry out an initial filter, and in the second phase, a CBR engine incorporates a neural network to complete the classification mechanism. AIDeM has been applied within the FUSION@ architecture to improve its current security mechanism. A prototype of the architecture was developed and applied to a case study. The results obtained are presented in this study

A practical exploration of the convergence of case-based reasoning and explainable artificial intelligence.

As Artificial Intelligence (AI) systems become increasingly complex, ensuring their decisions are transparent and understandable to users has become paramount. This paper explores the integration of Case-Based Reasoning (CBR) with Explainable Artificial Intelligence (XAI) through a real-world example, which presents an innovative CBR-driven XAI platform. This study investigates how CBR, a method that solves new problems based on the solutions of similar past problems, can be harnessed to enhance the explainability of AI systems. Though the literature has few works on the synergy between CBR and XAI, exploring the principles for developing a CBR-driven XAI platform is necessary. This exploration outlines the key features and functionalities, examines the alignment of CBR principles with XAI goals to make AI reasoning more transparent to users, and discusses methodological strategies for integrating CBR into XAI frameworks. Through a case study of our CBR-driven XAI platform, iSee: Intelligent Sharing of Explanation Experience, we demonstrate the practical application of these principles, highlighting the enhancement of system transparency and user trust. The platform elucidates the decision-making processes of AI models and adapts to provide explanations tailored to diverse user needs. Our findings emphasize the importance of interdisciplinary approaches in AI research and the significant role CBR can play in advancing the goals of XAI

DEKAS - An evolutionary case-based reasoning system to support protection scheme design

This paper describes a decision support system being developed in conjunction with two UK utility companies to aid the design of electrical power transmission protection systems. A brief overview of the application domain is provided, followed by a description of the work carried out to date concerning the development and deployment of the Design Engineering Knowledge Application System (DEKAS). The paper then discusses the provision of intelligent decision support to the design engineer through the application of case-based reasoning (CBR). The key benefits from this will be outlined in conjunction with a relevant case study

SISTEM PAKAR DIAGNOSIS DINI UNTUK PENYAKIT HEPATITIS MENGGUNAKAN METODE PENELUSURAN PENALARAN BERBASIS KASUS (CASE BASED REASONING)

Case-Based Reasoning an artificial intelligence method that’s use use previous experience in form of cases to understand and solve new problems. A case-based reasoner remembers former cases similar to the current problem and attempts to modify their solutions to fit for the current case. Case-based reasoning is not a new method in a branch of artificial intelligence. Application of CBR in an expert sistemto detect of a disease is appropriate, because the CBR is able to providea solutionsalso can adopt new knowledge from a new cases.This research aims to implement the concept of CBR that can be used in the utilization of CBR on an expert sistem to early diagnose the disease hepatitis. Where hepatitis is a disease that must be wary of. In this study the concepts in CBR described process steps in accordance with its case study, covering data storage techniques case, similarity search cases and generate solutions. Results of this study do not provide the appropriate solution. Since the core of the concept of CBR only provide solutions based on cases that have been saved to the knowledge base. To get the right solution, CBR requires a number of cases that is very much to be a knowledge base