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

Web-based CBR (case-based reasoning) as a tool with the application to tooling selection

Over the past few years, manufacturing companies have had to deal with an increasing demand for feature-rich products at low costs. The pressures exerted on their existing manufacturing processes have lead manufacturers to investigate internet-based solutions, in order to cope with growing competition. The decentralisation phenomenon also came up as a reason to implement networked-application, which has been the starting point for internet/intranet–based systems. Today, the availability of powerful and low cost 3D tools, database backend systems, along with web-based technologies, provides interesting opportunities to the manufacturing community, with solutions directly implementable at the core of their businesses and organisations. In this paper a web-based engineering approach is presented to developing a design support system using case-based reasoning (CBR) technology for helping in the decision-making process when choosing cutting tools. The system aims to provide on-line intelligent support for determining the most suitable configuration for turning operations, based on initial parameters and requirements for the cutting operation. The system also features a user-driven 3D turning simulator which allows testing the chosen insert for several turning operations. The system aims to be a useful e-manufacturing tool being able to quickly and responsively provide tooling data in a highly interactive way

Case Based Reasoning for Chemical Engineering Design

With current industrial environment (competition, lower profit margin, reduced time to market, decreased product life cycle, environmental constraints, sustainable development, reactivity, innovation…), we must decrease the time for design of new products or processes. While the design activity is marked out by several steps, this article proposed a decision support tool for the preliminary design step. This tool is based on the Case Based Reasoning (CBR) method. This method has demonstrated its effectiveness in other domains (medical, architecture…) and more recently in chemical engineering. This method, coming from Artificial Intelligence, is based on the reusing of earlier experiences to solve new problems. The goal of this article is to show the utility of such method for unit operation (for example) pre-design but also to propose several evolutions for CBR through a domain as complex as the chemical engineering is (because of its interactions, non linearity, intensification problems…). During the pre-design step, some parameters like operating conditions are not precisely known but we have an interval of possible values, worse we only have a partial description of the problem.. To take into account this imprecision in the problem description, the CBR method is coupled with the fuzzy sets theory. After a mere presentation of the CBR method, a practical implementation is described with the choice and the pre-design of packing for separation columns

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Optimization of fuzzy analogy in software cost estimation using linguistic variables

One of the most important objectives of software engineering community has been the increase of useful models that beneficially explain the development of life cycle and precisely calculate the effort of software cost estimation. In analogy concept, there is deficiency in handling the datasets containing categorical variables though there are innumerable methods to estimate the cost. Due to the nature of software engineering domain, generally project attributes are often measured in terms of linguistic values such as very low, low, high and very high. The imprecise nature of such value represents the uncertainty and vagueness in their elucidation. However, there is no efficient method that can directly deal with the categorical variables and tolerate such imprecision and uncertainty without taking the classical intervals and numeric value approaches. In this paper, a new approach for optimization based on fuzzy logic, linguistic quantifiers and analogy based reasoning is proposed to improve the performance of the effort in software project when they are described in either numerical or categorical data. The performance of this proposed method exemplifies a pragmatic validation based on the historical NASA dataset. The results were analyzed using the prediction criterion and indicates that the proposed method can produce more explainable results than other machine learning methods.Comment: 14 pages, 8 figures; Journal of Systems and Software, 2011. arXiv admin note: text overlap with arXiv:1112.3877 by other author

Retrieval, reuse, revision and retention in case-based reasoning

El original está disponible en www.journals.cambridge.orgCase-based reasoning (CBR) is an approach to problem solving that emphasizes the role of prior experience during future problem solving (i.e., new problems are solved by reusing and if necessary adapting the solutions to similar problems that were solved in the past). It has enjoyed considerable success in a wide variety of problem solving tasks and domains. Following a brief overview of the traditional problem-solving cycle in CBR, we examine the cognitive science foundations of CBR and its relationship to analogical reasoning. We then review a representative selection of CBR research in the past few decades on aspects of retrieval, reuse, revision, and retention.Peer reviewe

Experiments on Incremental Clustering

Clustering of very large document databases is essential to reduce the spacehime complexity of information retrieval. The periodic updating of clusters is required due to the dynamic nature of databases. An algorithm for incremental clustering at discrete times is introduced, Its complexity and cost analysis and an investigation of the expected behavior of the algorithm are provided. Through empirical testing, it is shown that the algorithm is achieving its purpose in terms of being cost effective, generating statistically valid clusters that are compatible with those of reclustering, and providing effective information retrieval

A dynamic adaptive framework for improving case-based reasoning system performance

An optimal performance of a Case-Based Reasoning (CBR) system means, the CBR system must be efficient both in time and in size, and must be optimally competent. The efficiency in time is closely related to an efficient and optimal retrieval process over the Case Base of the CBR system. Efficiency in size means that the Case Library (CL) size should be minimal. Therefore, the efficiency in size is closely related to optimal case learning policies, optimal meta-case learning policies, optimal case forgetting policies, etc. On the other hand, the optimal competence of a CBR system means that the number of problems that the CBR system can satisfactorily solve must be maximum. To improve or optimize all three dimensions in a CBR system at the same time is a difficult challenge because they are interrelated, and it becomes even more difficult when the CBR system is applied to a dynamic or continuous domain (data stream). In this thesis, a Dynamic Adaptive Case Library framework (DACL) is proposed to improve the CBR system performance coping especially with reducing the retrieval time, increasing the CBR system competence, and maintaining and adapting the CL to be efficient in size, especially in continuous domains. DACL learns cases and organizes them into dynamic cluster structures. The DACL is able to adapt itself to a dynamic environment, where new clusters, meta-cases or prototype of cases, and associated indexing structures (discriminant trees, k-d trees, etc.) can be formed, updated, or even removed. DACL offers a possible solution to the management of the large amount of data generated in an unsupervised continuous domain (data stream). In addition, we propose the use of a Multiple Case Library (MCL), which is a static version of a DACL, with the same structure but being defined statically to be used in supervised domains. The thesis work proposes some techniques for improving the indexation and the retrieval task. The most important indexing method is the NIAR k-d tree algorithm, which improves the retrieval time and competence, compared against the baseline approach (a flat CL) and against the well-known techniques based on using standard k-d tree strategies. The proposed Partial Matching Exploration (PME) technique explores a hierarchical case library with a tree indexing-structure aiming at not losing the most similar cases to a query case. This technique allows not only exploring the best matching path, but also several alternative partial matching paths to be explored. The results show an improvement in competence and time of retrieving of similar cases. Through the experimentation tests done, with a set of well-known benchmark supervised databases. The dynamic building of prototypes in DACL has been tested in an unsupervised domain (environmental domain) where the air pollution is evaluated. The core task of building prototypes in a DACL is the implementation of a stochastic method for the learning of new cases and management of prototypes. Finally, the whole dynamic framework, integrating all the main proposed approaches of the research work, has been tested in simulated unsupervised domains with several well-known databases in an incremental way, as data streams are processed in real life. The conclusions outlined that from the experimental results, it can be stated that the dynamic adaptive framework proposed (DACL/MCL), jointly with the contributed indexing strategies and exploration techniques, and with the proposed stochastic case learning policies, and meta-case learning policies, improves the performance of standard CBR systems both in supervised domains (MCL) and in unsupervised continuous domains (DACL).El rendimiento óptimo de un sistema de razonamiento basado en casos (CBR) significa que el sistema CBR debe ser eficiente tanto en tiempo como en tamaño, y debe ser competente de manera óptima. La eficiencia temporal está estrechamente relacionada con que el proceso de recuperación sobre la Base de Casos del sistema CBR sea eficiente y óptimo. La eficiencia en tamaño significa que el tamaño de la Base de Casos (CL) debe ser mínimo. Por lo tanto, la eficiencia en tamaño está estrechamente relacionada con las políticas óptimas de aprendizaje de casos y meta-casos, y las políticas óptimas de olvido de casos, etc. Por otro lado, la competencia óptima de un sistema CBR significa que el número de problemas que el sistema puede resolver de forma satisfactoria debe ser máximo. Mejorar u optimizar las tres dimensiones de un sistema CBR al mismo tiempo es un reto difícil, ya que están relacionadas entre sí, y se vuelve aún más difícil cuando se aplica el sistema de CBR a un dominio dinámico o continuo (flujo de datos). En esta tesis se propone el Dynamic Adaptive Case Library framework (DACL) para mejorar el rendimiento del sistema CBR especialmente con la reducción del tiempo de recuperación, aumentando la competencia del sistema CBR, manteniendo y adaptando la CL para ser eficiente en tamaño, especialmente en dominios continuos. DACL aprende casos y los organiza en estructuras dinámicas de clusters. DACL es capaz de adaptarse a entornos dinámicos, donde los nuevos clusters, meta-casos o prototipos de los casos, y las estructuras asociadas de indexación (árboles discriminantes, árboles k-d, etc.) se pueden formar, actualizarse, o incluso ser eliminados. DACL ofrece una posible solución para la gestión de la gran cantidad de datos generados en un dominio continuo no supervisado (flujo de datos). Además, se propone el uso de la Multiple Case Library (MCL), que es una versión estática de una DACL, con la misma estructura pero siendo definida estáticamente para ser utilizada en dominios supervisados. El trabajo de tesis propone algunas técnicas para mejorar los procesos de indexación y de recuperación. El método de indexación más importante es el algoritmo NIAR k-d tree, que mejora el tiempo de recuperación y la competencia, comparado con una CL plana y con las técnicas basadas en el uso de estrategias de árboles k-d estándar. Partial Matching Exploration (PME) technique, la técnica propuesta, explora una base de casos jerárquica con una indexación de estructura de árbol con el objetivo de no perder los casos más similares a un caso de consulta. Esta técnica no sólo permite explorar el mejor camino coincidente, sino también varios caminos parciales alternativos coincidentes. Los resultados, a través de la experimentación realizada con bases de datos supervisadas conocidas, muestran una mejora de la competencia y del tiempo de recuperación de casos similares. Además la construcción dinámica de prototipos en DACL ha sido probada en un dominio no supervisado (dominio ambiental), donde se evalúa la contaminación del aire. La tarea central de la construcción de prototipos en DACL es la implementación de un método estocástico para el aprendizaje de nuevos casos y la gestión de prototipos. Por último, todo el sistema, integrando todos los métodos propuestos en este trabajo de investigación, se ha evaluado en dominios no supervisados simulados con varias bases de datos de una manera gradual, como se procesan los flujos de datos en la vida real. Las conclusiones, a partir de los resultados experimentales, muestran que el sistema de adaptación dinámica propuesto (DACL / MCL), junto con las estrategias de indexación y de exploración, y con las políticas de aprendizaje de casos estocásticos y de meta-casos propuestas, mejora el rendimiento de los sistemas estándar de CBR tanto en dominios supervisados (MCL) como en dominios continuos no supervisados (DACL).Postprint (published version