Bayesian-Supported Retrieval in BNCreek: A Knowledge-Intensive Case-Based Reasoning System

Nikpour, Hoda; Aamodt, Agnar; Bach, Kerstin

doi:10.1007/978-3-030-01081-2_22

Cited by 8 publications

(9 citation statements)

References 11 publications

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“…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.…”

Section: Related Workmentioning

confidence: 99%

Learning similarity measures from data

et al. 2019

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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.

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Section: Related Workmentioning

confidence: 99%

Learning similarity measures from data

et al. 2019

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“…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.…”

Section: Functional Architecturementioning

confidence: 99%

“…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].…”

Section: System Evaluationmentioning

confidence: 99%

Fault diagnosis under uncertain situations within a Bayesian knowledge-intensive CBR system

Nikpour

Aamodt

2021

Prog Artif Intell

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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.

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“…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.…”

Section: Retrievementioning

confidence: 99%

“…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].…”

Section: Introductionmentioning

confidence: 99%

Inference and reasoning in a Bayesian knowledge-intensive CBR system

Nikpour

Aamodt

2021

Prog Artif Intell

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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.

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Bayesian-Supported Retrieval in BNCreek: A Knowledge-Intensive Case-Based Reasoning System

Cited by 8 publications

References 11 publications

Learning similarity measures from data

Learning similarity measures from data

Fault diagnosis under uncertain situations within a Bayesian knowledge-intensive CBR system

Inference and reasoning in a Bayesian knowledge-intensive CBR system

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