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This study presents a case-based reasoning (CBR) system that makes use of general domain knowledge - referred to as a knowledge-intensive CBR system. The system applies a Bayesian analysis aimed at increasing the accuracy of the similarity assessment. The idea is to employ the Bayesian posterior distribution for each case symptom to modify the case...
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... similarity assessment in BNCreek follows an "explanation engine" (Fig. 4) with an Activate-Explain-Focus cycle [2]. Activate finds the directly matched fea- tures between input and retrieved cases then the Explain tries to account for the not directly matched features of the input and retrieved cases. Focus applies the preferences or external constraints to adjust the ranking of the cases. BNCreek considers ...
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... Some methods under the graphical approach takes into consideration of the probabilistic element into causal relationships where the causal effect is a probability rather than a certainty. Some of the methods under graphical approaches include [27], Expectation Maximization (EM) [28], and Structural Expectation Maximization (SEM) [29]; an interesting survey for applications that fall under this category can be found at [30]. In general, the causal prediction accuracy of graphical approaches is somewhat inconsistent and depends heavily on the manual fine tuning of hyper-parameters. ...
Causal discovery between collections of time-series data can help diagnose causes of symptoms and hopefully prevent faults before they occur. However, reliable causal discovery can be very challenging, especially when the data acquisition rate varies (i.e., non-uniform data sampling), or in the presence of missing data points (e.g., sparse data sampling). To address these issues, we proposed a new system comprised of two parts, the first part fills missing data with a Gaussian Process Regression, and the second part leverages an Echo State Network, which is a type of reservoir computer (i.e., used for chaotic system modeling) for Causal discovery. We evaluate the performance of our proposed system against three other off-the-shelf causal discovery algorithms, namely, structural expectation-maximization, sub-sampled linear auto-regression absolute coefficients, and multivariate Granger Causality with vector auto-regressive using the Tennessee Eastman chemical dataset; we report on their corresponding Matthews Correlation Coefficient(MCC) and Receiver Operating Characteristic curves (ROC) and show that the proposed system outperforms existing algorithms, demonstrating the viability of our approach to discover causal relationships in a complex system with missing entries.
... From a technical perspective, the use of augmented cases is a key novelty of BCBR that can be viewed as a data-driven approach that uses feature construction to embed solution knowledge in cases for case retrieval in CBR [8,15,18]. The use of BI to estimate probability ensures transparency because the estimates are made by counting cases in the data set. ...
... Such frameworks also provide explanations, where CBR has been used to achieve explanation goals [22] or generate explanations [19]. Nikpour et al. [18] use Bayesian posterior distributions to modify or add features to input case descriptions to increase accuracy of similarity assessments in case retrieval. They also use the same approach to provide explanations for case failures in different domains [17]. ...
Checklists are used to aid the fulfillment of safety critical activities in a variety of different applications, such as aviation, health care or labour inspections. However, optimizing a checklist for a specific purpose can be challenging. Checklists also need to be trustworthy and user friendly to promote user compliance. With labour inspections as a starting point, we introduce the Checklist Construction Problem. To address the problem, we seek to optimize the content of labour inspection checklists in order to improve the working conditions in every organisation targeted for inspections. To do so, we introduce a hybrid framework called BCBR to construct trustworthy checklists. BCBR is based on case-based reasoning (CBR) and Bayesian inference (BI) and constructs new checklists based on past cases. A key novelty of BCBR is the use of BI for constructing new features in past cases. The augmented past cases are retrieved via CBR to construct new checklists, which ensures justification for the content of the checklists and promotes trust. Experiments suggest that BCBR is more effective than any other baseline we tested, in terms of constructing trustworthy checklists.
... Authors in [28] conducted trials to combine CBR and semantic networks. The goal was to generate more detailed and meaningful explanations. ...
A lot of computational models recently are undergoing rapid development. However, there is a conceptual and analytical gap in understanding the driving forces behind them. This paper focuses on the integration between computer science and social science (namely, education) for strengthening the visibility, recognition, and understanding the problems of simulation and modelling in social (educational) decision processes. The objective of the paper covers topics and streams on social-behavioural modelling and computational intelligence applications in education. To obtain the benefits of real, factual data for modeling student learning styles, this paper investigates exemplar-based approaches and possibilities to combine them with case-based reasoning methods for automatically predicting student learning styles in virtual learning environments. A comparative analysis of approaches combining exemplar-based modelling and case-based reasoning leads to the choice of the Bayesian Case model for diagnosing a student’s learning style based on the data about the student’s behavioral activities performed in an e-learning environment.
... The cases are represented by nodes and are connected to the other two modules through the case features. A case feature consists of a concept from the knowledge model, a relation type, and a relevance factor that represents the importance of a feature for a stored case The graphical representation of the system's general architecture [21] Fig. 2 The system's structural architecture [9]. The case base module is a set of cases that are collected to be utilized for inference and reasoning purposes. ...
... The semantic network and Bayesian network are well-defined extendible representation languages in which the properties of a new concept can be added into the network without imposing a heavy change to the rest. Also, for big domains, the networks could split up and distribute between the individual systems [21]. This makes the network representation language a good candidate for a knowledge-based designer to model the knowledge. ...
... 2. The rootsquare error (RSE) and weighted error (WE) are applied to measure the accuracy of the similarity degrees. The results of the Retrieve phase evaluation are presented in [21] [https:// link.springer.com/article/10.1007/s13748-020-00223-1]. ...
This paper presents fault diagnosis and problem solving under uncertainty by a Bayesian supported knowledge-intensive case-based reasoning (CBR) system called BNCreek. In this system, the main goal is to diagnose the causal failures behind the symptoms in complex and uncertain domains. The system’s architecture is described in three aspects: the general, structural, and functional architectures. The domain knowledge is represented by formally defined methods. An integration of semantic networks, Bayesian networks, and CBR is employed to deal with the domain uncertainty. An experiment is conducted from the oil well drilling domain, which is a complex and uncertain area as an application domain. The system is evaluated against the expert estimations to find the most efficient solutions for the problems. The obtained results reveal the capability of the system in diagnosing causal failures.
... A prominent example is CREEK (Aamodt 2004), which employs substantial general knowledge and whose similarity function is designed to provide an explanation. This has been further developed by (Nikpour, Aamodt, and Bach 2018) using a Bayesian retrieval function in BNCreek. ...
Various literature surveys state and confirm a rapid increase in research on explainable artificial intelligence (XAI) in recent years. One possible motivation for this change are legal regulations, including the general data protection regulation (GDPR) but also similar regulations outside of Europe. Another possible reason is the decreasing trust in machine learning systems since both their algorithms and they models they include are often opaque. The desire to retrieve an explanation for a given decision reaches back to the era of expert systems in the 1980s. Decisions made by experts often rely on their stored experiences, yet most XAI approaches cannot provide explanations based on specific experiences because they do not retain them. In contrast, explainable case-based reasoning (XCBR) approaches can provide such explanations , and thus is of interest to XAI researchers. We present a taxonomy of XCBR approaches by categorizing and presenting current methodologies and implementations based on an extensive literature review. This taxonomy can be used by XAI researchers and CBR researchers who are explicitly interested in the generation and use of explanations.
... The first attempts to investigate effects of Bayesian analysis in cooperation with the CBR and the semantic network inference methods can be found in [6,7]. ...
... The smelly food is the evidence node that is shown in blue. The example is adapted from [6] enough, little garlic, and little onion probabilities on the left side are 70%, 67%, 60%, and 60%, respectively. While after propagating the evidence on the right side, they are 100% (shown in blue as the evidence node), 76%, 63%, and 63%, respectively. ...
This paper presents the inference and reasoning methods in a Bayesian supported knowledge-intensive case-based reasoning (CBR) system called BNCreek. The inference and reasoning process in this system is a combination of three methods. The semantic network inference methods and the CBR method are employed to handle the difficulties of inferencing and reasoning in uncertain domains. The Bayesian network inference methods are employed to make the process more accurate. An experiment from oil well drilling as a complex and uncertain application domain is conducted. The system is evaluated against expert estimations and compared with seven other corresponding systems. The normalized discounted cumulative gain (NDCG) as a rank-based metric, the weighted error (WE), and root-square error (RSE) as the statistical metrics are employed to evaluate different aspects of the system capabilities. The results show the efficiency of the developed inference and reasoning methods.
... A general overview and examples of this type of similarity measure can be found in [7]. Nikpour et al. [23] presented an alternative method which includes enrichment of the cases/data points via Bayesian networks. ...
Defining similarity measures is a requirement for some machine learning methods. One such method is case-based reasoning (CBR) where the similarity measure is used to retrieve the stored case or a set of cases most similar to the query case. Describing a similarity measure analytically is challenging, even for domain experts working with CBR experts. However, datasets are typically gathered as part of constructing a CBR or machine learning system. These datasets are assumed to contain the features that correctly identify the solution from the problem features; thus, they may also contain the knowledge to construct or learn such a similarity measure. The main motivation for this work is to automate the construction of similarity measures using machine learning. Additionally, we would like to do this while keeping training time as low as possible. Working toward this, our objective is to investigate how to apply machine learning to effectively learn a similarity measure. Such a learned similarity measure could be used for CBR systems, but also for clustering data in semi-supervised learning, or one-shot learning tasks. Recent work has advanced toward this goal which relies on either very long training times or manually modeling parts of the similarity measure. We created a framework to help us analyze the current methods for learning similarity measures. This analysis resulted in two novel similarity measure designs: The first design uses a pre-trained classifier as basis for a similarity measure, and the second design uses as little modeling as possible while learning the similarity measure from data and keeping training time low. Both similarity measures were evaluated on 14 different datasets. The evaluation shows that using a classifier as basis for a similarity measure gives state-of-the-art performance. Finally, the evaluation shows that our fully data-driven similarity measure design outperforms state-of-the-art methods while keeping training time low.
We present a novel and practical dialogue system specifically designed for teachers and parents to solve students’ problems in moral education. Guided by the case-based reasoning theory, we collect the high-quality cases and teaching strategies from heterogeneous sources, and then construct the dedicated knowledge graph to manage the large volume of information in this domain. By leveraging on the latest natural language processing techniques, we finally implement a task-oriented dialogue system to precisely understand user’s problem and subsequently recommend possible solutions. We show the great promise of the system for K-12 education and demonstrate how the system solves the problem raised by the teacher for moral education.
