On Combining Knowledge-Engineered and Network-Extracted Features for Retrieval

Wilkerson, Zachary; Leake, David; Crandall, David J.

doi:10.1007/978-3-030-86957-1_17

Cited by 6 publications

(1 citation statement)

References 14 publications

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“…Lepage et al (2020) proposed adaptation before retrieval in the CBR process of sentence correction, that is, using the adaptation-guided retrieval method to achieve French correction. Based on nonsymbolic types such as images, Wilkerson et al (2021) proposed a weighting strategy that performs better in feature-intensive spaces to achieve case retrieval, which obtains the features learned from data using DL to supplement the existing knowledge engineering features and learns the feature weights of both through neural networks. Based on incomplete case retrieval, Low et al (2019) proposed a multiple retrieval CBR (MRCBR) framework for incomplete databases, which not only combines the advantages of multiple interpolation and CBR but also retains the data distribution and database structure.…”

Section: Case Retrievalmentioning

confidence: 99%

A review of the development and future challenges of case-based reasoning

Yan

Cheng

2023

Preprint

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Case-based reasoning (CBR), which is based on the cognitive assumption that similar problems have similar solutions, is an important problem-solving and learning method in the field of artificial intelligence. In this article, the development of CBR is mainly reviewed, and the major challenges of CBR are summarized. The paper is organized into four parts. First, the basic framework and concepts of CBR are introduced. Then, the developed technology and innovative work that were formed in solving problems by CBR are summarized. Moreover, the application fields of CBR are sorted. Finally, according to the idea of deep learning and interpretable artificial intelligence, the main challenges for the future development of CBR are proposed.

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Section: Case Retrievalmentioning

confidence: 99%