CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

Case-based analysis in user requirements modelling for knowledge construction

Context: Learning can be regarded as knowledge construction in which prior knowledge and experience serve as basis for the learners to expand their knowledge base. Such a process of knowledge construction has to take place continuously in order to enhance the learners’ competence in a competitive working environment. As the information consumers, the individual users demand personalised information provision which meets their own specific purposes, goals, and expectations. Objectives: The current methods in requirements engineering are capable of modelling the common user’s behaviour in the domain of knowledge construction. The users’ requirements can be represented as a case in the defined structure which can be reasoned to enable the requirements analysis. Such analysis needs to be enhanced so that personalised information provision can be tackled and modelled. However, there is a lack of suitable modelling methods to achieve this end. This paper presents a new ontological method for capturing individual user’s requirements and transforming the requirements onto personalised information provision specifications. Hence the right information can be provided to the right user for the right purpose. Method: An experiment was conducted based on the qualitative method. A medium size of group of users participated to validate the method and its techniques, i.e. articulates, maps, configures, and learning content. The results were used as the feedback for the improvement. Result: The research work has produced an ontology model with a set of techniques which support the functions for profiling user’s requirements, reasoning requirements patterns, generating workflow from norms, and formulating information provision specifications. Conclusion: The current requirements engineering approaches provide the methodical capability for developing solutions. Our research outcome, i.e. the ontology model with the techniques, can further enhance the RE approaches for modelling the individual user’s needs and discovering the user’s requirements

Enriching Retrieval Process for Case Based Reasoning by using Vertical Association Knowledge with Correlation

In case-based reasoning (CBR) the most important step is retrieval. The main purpose of the case-based reasoning is to get the cases which are useful to find out the solution for the given problem. For retrieving relevant data the CBR systems mainly uses the similarity knowledge. Most of the retrieving systems use similarity knowledge and association rules for retrieving the required cases. But the existing algorithms strongly rely on similarity knowledge and ignore the other forms of knowledge that can be used to improve the retrieval performance. The well known algorithm Apriori algorithm is used to extract desired relevant cases based on the knowledge system of the association rules with the efficient correlation methods. The goal of this paper is to provide detailed review about retrieving useful cases by using different methods and showing the effectiveness of each algorithm

Case-based reasoning for meta-heuristics self-parameterization in a multi-agent scheduling system

A novel agent-based approach to Meta-Heuristics self-configuration is proposed in this work. Meta-heuristics are examples of algorithms where parameters need to be set up as efficient as possible in order to unsure its performance. This paper presents a learning module for self-parameterization of Meta-heuristics (MHs) in a Multi-Agent System (MAS) for resolution of scheduling problems. The learning is based on Case-based Reasoning (CBR) and two different integration approaches are proposed. A computational study is made for comparing the two CBR integration perspectives. In the end, some conclusions are reached and future work outlined

Competence-Preserving Case-Deletion Strategy for Case-Base Maintenance.

International audienceThe main goal of a Case-Based Reasoning (CBR) system is to provide criteria for evaluating the internal behavior and task efficiency of a particular system for a given initial case base and sequence of a solved problems. The choice of Case Base Maintenance (CBM) strategies is driven by the maintainer's performance goals for the system and by constraints on the system's design and the task environment. This paper gives an overview of CBM works and proposes a case deletion strategy based on a competence criterion using a novel approach. The proposed method combines an algorithm with a Competence Metric (CM). Series of tests are conducted using four standard data-sets as well as a locally constructed one, on which, three case base maintenance approaches will be tested and evaluated by competence and performance criteria. Thereafter competence and performance experimental study shows how this method compares favorably to more traditional methods

A concept drift-tolerant case-base editing technique

© 2015 Elsevier B.V. All rights reserved. The evolving nature and accumulating volume of real-world data inevitably give rise to the so-called "concept drift" issue, causing many deployed Case-Based Reasoning (CBR) systems to require additional maintenance procedures. In Case-base Maintenance (CBM), case-base editing strategies to revise the case-base have proven to be effective instance selection approaches for handling concept drift. Motivated by current issues related to CBR techniques in handling concept drift, we present a two-stage case-base editing technique. In Stage 1, we propose a Noise-Enhanced Fast Context Switch (NEFCS) algorithm, which targets the removal of noise in a dynamic environment, and in Stage 2, we develop an innovative Stepwise Redundancy Removal (SRR) algorithm, which reduces the size of the case-base by eliminating redundancies while preserving the case-base coverage. Experimental evaluations on several public real-world datasets show that our case-base editing technique significantly improves accuracy compared to other case-base editing approaches on concept drift tasks, while preserving its effectiveness on static tasks

A Case-Based Approach to Business Process Monitoring

International audienceThe agile workflow technology deals with flexible workflow adaptation and overriding, in case of foreseen as well as unforeseen changes and problems in the operating business environment. One key issue that an agile workflow system should address is Business Process (BP) monitoring. This consists in properly highlighting and organizing non-compliances and adaptations with respect to the default process schema. Such an activity can be the starting point for other very critical tasks, such as quality assessment and process reengineering. In this paper, we introduce an automated support to BP monitoring, which exploits the Case-based Reasoning (CBR) methodology. CBR is particularly well suited for managing exceptional situations, and has been proposed in the literature for process change reuse and workflow adaptation support. Our work extends these functionalities by retrieving traces of process execution similar to the current one, which can then be automatically clustered. Retrieval and clustering results can provide support both to end users, in the process instance execution phase, and to process engineers, in (formal) process quality evaluation and long term process schema redefinition. Our approach in practice is illustrated by means of a case study in the field of stroke management

Case-based maintenance : Structuring and incrementing the Case.

International audienceTo avoid performance degradation and maintain the quality of results obtained by the case-based reasoning (CBR) systems, maintenance becomes necessary, especially for those systems designed to operate over long periods and which must handle large numbers of cases. CBR systems cannot be preserved without scanning the case base. For this reason, the latter must undergo maintenance operations.The techniques of case base’s dimension optimization is the analog of instance reduction size methodology (in the machine learning community). This study links these techniques by presenting case-based maintenance in the framework of instance based reduction, and provides: first an overview of CBM studies, second, a novel method of structuring and updating the case base and finally an application of industrial case is presented.The structuring combines a categorization algorithm with a measure of competence CM based on competence and performance criteria. Since the case base must progress over time through the addition of new cases, an auto-increment algorithm is installed in order to dynamically ensure the structuring and the quality of a case base. The proposed method was evaluated through a case base from an industrial plant. In addition, an experimental study of the competence and the performance was undertaken on reference benchmarks. This study showed that the proposed method gives better results than the best methods currently found in the literature