11 research outputs found

    Representing cases from texts in case-based reasoning

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    Paper presented at the Third International Conference of Industrial Engineering and XVII ENEGEP, Rio Grande do Sul, Brazil.Case representation is a Case-Based Reasoning (CBR) problem area that refers to selecting proper descriptors to describe and index cases. The complexity of case representation has been preventing CBR systems from solving problems when large case bases are required. We present the development and implementation of a methodology to automatically convert legal texts into cases based on indexing methods and domain expert knowledge. The methodology is tailored to the domain of law although it can be extended to be applied to other domains as well

    A large case-based reasoner for legal cases

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    Case-Based Reasoning Research and Development: Proceedings of the 2nd International Conference on Case-Based Reasoning, ICCBR 1997: pp. 190-199.In this paper we propose a large case-based reasoner for the legal domain. Analyzing legal texts for indexing purposes makes the implementation of large case bases a complex task. We present a methodology to automatically convert legal texts into legal cases guided by domain expert knowledge in a rule-based system with Natural Language Processing (NLP) techniques. This methodology can be generalized to be applied in different domains making Case-Based Reasoning (CBR) paradigm a powerful technology to solve real world problems with large knowledge sources

    Meta-data to enhance case-based prediction.

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    The focus of this thesis is to measure the regularity of case bases used in Case-Based Prediction (CBP) systems and the reliability of their constituent cases prior to the system's deployment to influence user confidence on the delivered solutions. The reliability information, referred to as meta-data, is then used to enhance prediction accuracy. CBP is a strain of Case-Based Reasoning (CBR) that differs from the latter only in the solution feature which is a continuous value. Several factors make implementing such systems for prediction domains a challenge. Typically, the problem and solution spaces are unbounded in prediction problems that make it difficult to determine the portions of the domain represented by the case base. In addition, such problem domains often exhibit complex and poorly understood interactions between features and contain noise. As a result, the overall regularity in the case base is distorted which poses a hindrance to delivery of good quality solutions. Hence in this research, techniques have been presented that address the issue of irregularity in case bases with an objective to increase prediction accuracy of solutions. Although, several techniques have been proposed in the CBR literature to deal with irregular case bases, they are inapplicable to CBP problems. As an alternative, this research proposes the generation of relevant case-specific meta-data. The meta-data is made use of in Mantel's randomisation test to objectively measure regularity in the case base. Several novel visualisations using the meta-data have been presented to observe the degree of regularity and help identify suspect unreliable cases whose reuse may very likely yield poor solutions. Further, performances of individual cases are recorded to judge their reliability, which is reflected upon before selecting them for reuse along with their distance from the problem case. The intention is to overlook unreliable cases in favour of relatively distant yet more reliable ones for reuse to enhance prediction accuracy. The proposed techniques have been demonstrated on software engineering data sets where the aim is to predict the duration of a software project on the basis of past completed projects recorded in the case base. Software engineering is a human-centric, volatile and dynamic discipline where many unrecorded factors influence productivity. This degrades the regularity in case bases where cases are disproportionably spread out in the problem and solution spaces resulting in erratic prediction quality. Results from administering the proposed techniques were helpful to gain insight into the three software engineering data sets used in this analysis. The Mantel's test was very effective at measuring overall regularity within a case base, while the visualisations were learnt to be variably valuable depending upon the size of the data set. Most importantly, the proposed case discrimination system, that intended to reuse only reliable similar cases, was successful at increasing prediction accuracy for all three data sets. Thus, the contributions of this research are some novel approaches making use of meta-data to firstly provide the means to assess and visualise irregularities in case bases and cases from prediction domains and secondly, provide a method to identify unreliable cases to avoid their reuse in favour to more reliable cases to enhance overall prediction accuracy

    Meta-data to enhance case-based prediction

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    The focus of this thesis is to measure the regularity of case bases used in Case-Based Prediction (CBP) systems and the reliability of their constituent cases prior to the system's deployment to influence user confidence on the delivered solutions. The reliability information, referred to as meta-data, is then used to enhance prediction accuracy. CBP is a strain of Case-Based Reasoning (CBR) that differs from the latter only in the solution feature which is a continuous value. Several factors make implementing such systems for prediction domains a challenge. Typically, the problem and solution spaces are unbounded in prediction problems that make it difficult to determine the portions of the domain represented by the case base. In addition, such problem domains often exhibit complex and poorly understood interactions between features and contain noise. As a result, the overall regularity in the case base is distorted which poses a hindrance to delivery of good quality solutions. Hence in this research, techniques have been presented that address the issue of irregularity in case bases with an objective to increase prediction accuracy of solutions. Although, several techniques have been proposed in the CBR literature to deal with irregular case bases, they are inapplicable to CBP problems. As an alternative, this research proposes the generation of relevant case-specific meta-data. The meta-data is made use of in Mantel's randomisation test to objectively measure regularity in the case base. Several novel visualisations using the meta-data have been presented to observe the degree of regularity and help identify suspect unreliable cases whose reuse may very likely yield poor solutions. Further, performances of individual cases are recorded to judge their reliability, which is reflected upon before selecting them for reuse along with their distance from the problem case. The intention is to overlook unreliable cases in favour of relatively distant yet more reliable ones for reuse to enhance prediction accuracy. The proposed techniques have been demonstrated on software engineering data sets where the aim is to predict the duration of a software project on the basis of past completed projects recorded in the case base. Software engineering is a human-centric, volatile and dynamic discipline where many unrecorded factors influence productivity. This degrades the regularity in case bases where cases are disproportionably spread out in the problem and solution spaces resulting in erratic prediction quality. Results from administering the proposed techniques were helpful to gain insight into the three software engineering data sets used in this analysis. The Mantel's test was very effective at measuring overall regularity within a case base, while the visualisations were learnt to be variably valuable depending upon the size of the data set. Most importantly, the proposed case discrimination system, that intended to reuse only reliable similar cases, was successful at increasing prediction accuracy for all three data sets. Thus, the contributions of this research are some novel approaches making use of meta-data to firstly provide the means to assess and visualise irregularities in case bases and cases from prediction domains and secondly, provide a method to identify unreliable cases to avoid their reuse in favour to more reliable cases to enhance overall prediction accuracy.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

    Automatic caption generation for content-based image information retrieval.

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    Ma, Ka Ho.Thesis (M.Phil.)--Chinese University of Hong Kong, 1999.Includes bibliographical references (leaves 82-87).Abstract and appendix in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Objective of This Research --- p.4Chapter 1.2 --- Organization of This Thesis --- p.5Chapter 2 --- Background --- p.6Chapter 2.1 --- Textual - Image Query Approach --- p.7Chapter 2.1.1 --- Yahoo! Image Surfer --- p.7Chapter 2.1.2 --- QBIC (Query By Image Content) --- p.8Chapter 2.2 --- Feature-based Approach --- p.9Chapter 2.2.1 --- Texture Thesaurus for Aerial Photos --- p.9Chapter 2.3 --- Caption-aided Approach --- p.10Chapter 2.3.1 --- PICTION (Picture and capTION) --- p.10Chapter 2.3.2 --- MARIE --- p.11Chapter 2.4 --- Summary --- p.11Chapter 3 --- Caption Generation --- p.13Chapter 3.1 --- System Architecture --- p.13Chapter 3.2 --- Domain Pool --- p.15Chapter 3.3 --- Image Feature Extraction --- p.16Chapter 3.3.1 --- Preprocessing --- p.16Chapter 3.3.2 --- Image Segmentation --- p.17Chapter 3.4 --- Classification --- p.24Chapter 3.4.1 --- Self-Organizing Map (SOM) --- p.26Chapter 3.4.2 --- Learning Vector Quantization (LVQ) --- p.28Chapter 3.4.3 --- Output of the Classification --- p.30Chapter 3.5 --- Caption Generation --- p.30Chapter 3.5.1 --- Phase One: Logical Form Generation --- p.31Chapter 3.5.2 --- Phase Two: Simplification --- p.32Chapter 3.5.3 --- Phase Three: Captioning --- p.33Chapter 3.6 --- Summary --- p.35Chapter 4 --- Query Examples --- p.37Chapter 4.1 --- Query Types --- p.37Chapter 4.1.1 --- Non-content-based Retrieval --- p.38Chapter 4.1.2 --- Content-based Retrieval --- p.38Chapter 4.2 --- Hierarchy Graph --- p.41Chapter 4.3 --- Matching --- p.42Chapter 4.4 --- Summary --- p.48Chapter 5 --- Evaluation --- p.49Chapter 5.1 --- Experimental Set-up --- p.50Chapter 5.2 --- Experimental Results --- p.51Chapter 5.2.1 --- Segmentation --- p.51Chapter 5.2.2 --- Classification --- p.53Chapter 5.2.3 --- Captioning --- p.55Chapter 5.2.4 --- Overall Performance --- p.56Chapter 5.3 --- Observations --- p.57Chapter 5.4 --- Summary --- p.58Chapter 6 --- Another Application --- p.59Chapter 6.1 --- Police Force Crimes Investigation --- p.59Chapter 6.1.1 --- Image Feature Extraction --- p.61Chapter 6.1.2 --- Caption Generation --- p.64Chapter 6.1.3 --- Query --- p.66Chapter 6.2 --- An Illustrative Example --- p.68Chapter 6.3 --- Summary --- p.72Chapter 7 --- Conclusions --- p.74Chapter 7.1 --- Contribution --- p.77Chapter 7.2 --- Future Work --- p.78Bibliography --- p.81Appendices --- p.88Chapter A --- Segmentation Result Under Different Parametes --- p.89Chapter B --- Segmentation Time of 10 Randomly Selected Images --- p.90Chapter C --- Sample Captions --- p.9

    A case-based reasoning methodology to formulating polyurethanes

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    Formulation of polyurethanes is a complex problem poorly understood as it has developed more as an art rather than a science. Only a few experts have mastered polyurethane (PU) formulation after years of experience and the major raw material manufacturers largely hold such expertise. Understanding of PU formulation is at present insufficient to be developed from first principles. The first principle approach requires time and a detailed understanding of the underlying principles that govern the formulation process (e.g. PU chemistry, kinetics) and a number of measurements of process conditions. Even in the simplest formulations, there are more that 20 variables often interacting with each other in very intricate ways. In this doctoral thesis the use of the Case-Based Reasoning and Artificial Neural Network paradigm is proposed to enable support for PUs formulation tasks by providing a framework for the collection, structure, and representation of real formulating knowledge. The framework is also aimed at facilitating the sharing and deployment of solutions in a consistent and referable way, when appropriate, for future problem solving. Two basic problems in the development of a Case-Based Reasoning tool that uses past flexible PU foam formulation recipes or cases to solve new problems were studied. A PU case was divided into a problem description (i. e. PU measured mechanical properties) and a solution description (i. e. the ingredients and their quantities to produce a PU). The problems investigated are related to the retrieval of former PU cases that are similar to a new problem description, and the adaptation of the retrieved case to meet the problem constraints. For retrieval, an alternative similarity measure based on the moment's description of a case when it is represented as a two dimensional image was studied. The retrieval using geometric, central and Legendre moments was also studied and compared with a standard nearest neighbour algorithm using nine different distance functions (e.g. Euclidean, Canberra, City Block, among others). It was concluded that when cases were represented as 2D images and matching is performed by using moment functions in a similar fashion to the approaches studied in image analysis in pattern recognition, low order geometric and Legendre moments and central moments of any order retrieve the same case as the Euclidean distance does when used in a nearest neighbour algorithm. This means that the Euclidean distance acts a low moment function that represents gross level case features. Higher order (moment's order>3) geometric and Legendre moments while enabling finer details about an image to be represented had no standard distance function counterpart. For the adaptation of retrieved cases, a feed-forward back-propagation artificial neural network was proposed to reduce the adaptation knowledge acquisition effort that has prevented building complete CBR systems and to generate a mapping between change in mechanical properties and formulation ingredients. The proposed network was trained with the differences between problem descriptions (i.e. mechanical properties of a pair of foams) as input patterns and the differences between solution descriptions (i.e. formulation ingredients) as the output patterns. A complete data set was used based on 34 initial formulations and a 16950 epochs trained network with 1102 training exemplars, produced from the case differences, gave only 4% error. However, further work with a data set consisting of a training set and a small validation set failed to generalise returning a high percentage of errors. Further tests on different training/test splits of the data also failed to generalise. The conclusion reached is that the data as such has insufficient common structure to form any general conclusions. Other evidence to suggest that the data does not contain generalisable structure includes the large number of hidden nodes necessary to achieve convergence on the complete data set.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

    Using causal knowledge to improve retrieval and adaptation in case-based reasoning systems for a dynamic industrial process

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    Case-based reasoning (CBR) is a reasoning paradigm that starts the reasoning process by examining past similar experiences. The motivation behind this thesis lies in the observation that causal knowledge can guide case-based reasoning in dealing with large and complex systems as it guides humans. In this thesis, case-bases used for reasoning about processes where each case consists of a temporal sequence are considered. In general, these temporal sequences include persistent and transitory (non-persistent) attributes. As these sequences tend to be long, it is unlikely to find a single case in the case-base that closely matches the problem case. By utilizing causal knowledge in the form of a dynamic Bayesian network (DBN) and exploiting the independence implied by the structure of the network and known attributes, this system matches independent portions of the problem case to corresponding sub-cases from the case-base. However, the matching of sub-cases has to take into account the persistence properties of attributes. The approach is then applied to a real life temporal process situation involving an automotive curing oven, in which a vehicle moves through stages within the oven to satisfy some thermodynamic relationships and requirements that change from stage to stage. In addition, testing has been conducted using data randomly generated from known causal networks. (Abstract shortened by UMI.) Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .T54. Source: Masters Abstracts International, Volume: 45-01, page: 0366. Thesis (M.Sc.)--University of Windsor (Canada), 2006

    A reflective process memory in decision making

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    SIGLEAvailable from British Library Document Supply Centre-DSC:DXN024000 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

    Machine Medical Ethics

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    In medical settings, machines are in close proximity with human beings: with patients who are in vulnerable states of health, who have disabilities of various kinds, with the very young or very old, and with medical professionals. Machines in these contexts are undertaking important medical tasks that require emotional sensitivity, knowledge of medical codes, human dignity, and privacy. As machine technology advances, ethical concerns become more urgent: should medical machines be programmed to follow a code of medical ethics? What theory or theories should constrain medical machine conduct? What design features are required? Should machines share responsibility with humans for the ethical consequences of medical actions? How ought clinical relationships involving machines to be modeled? Is a capacity for empathy and emotion detection necessary? What about consciousness? The essays in this collection by researchers from both humanities and science describe various theoretical and experimental approaches to adding medical ethics to a machine, what design features are necessary in order to achieve this, philosophical and practical questions concerning justice, rights, decision-making and responsibility, and accurately modeling essential physician-machine-patient relationships. This collection is the first book to address these 21st-century concerns

    An integrated system to design machine layouts for modular special purpose machines

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    This thesis introduces the development of an integrated system for the design of layouts for special purpose machines (SPMs). SPMs are capable of performing several machining operations (such as drilling, milling, and tapping) at the same time. They consist of elements that can be arranged in different layouts. Whilst this is a unique feature that makes SPMs modular, a high level of knowledge and experience is required to rearrange the SPM elements in different configurations, and also to select appropriate SPM elements when product demand changes and new layouts are required. In this research, an integrated system for SPM layout design was developed by considering the following components: an expert system tool, an assembly modelling approach for SPM layouts, an artificial intelligence tool, and a CAD design environment. SolidWorks was used as the 3D CAD environment. VisiRule was used as the expert system tool to make decisions about the selection of SPM elements. An assembly modelling approach was developed with an SPM database using a linked list structure and assembly relationships graph. A case-based reasoning (CBR) approach was developed and applied to automate the selection of SPM layouts. These components were integrated using application programing interface (API) features and Visual Basic programming language. The outcome of the application of the novel approach that was developed in this thesis is reducing the steps for the assembly process of the SPM elements and reducing the time for designing SPM layouts. As a result, only one step is required to assemble any two SPM elements and the time for the selection process of SPM layouts is reduced by approximately 75% compared to the traditional processes. The integrated system developed in this thesis will help engineers in design and manufacturing fields to design SPM layouts in a more time-effective manner
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