Case-Based Reasoning for Knowledge-Intensive Template Selection During Text Generation

2006 ◽  
pp. 151-165 ◽  
Author(s):  
Raquel Hervás ◽  
Pablo Gervás
Keyword(s):  
Case Based Reasoning ◽  
Text Generation ◽  
Template Selection ◽  
Knowledge Intensive ◽  
Case Based

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

2021 ◽  
Author(s):  
Hoda Nikpour ◽  
Agnar Aamodt
Keyword(s):  
Fault Diagnosis ◽  
Domain Knowledge ◽  
Semantic Networks ◽  
Efficient Solutions ◽  
Case Based Reasoning ◽  
Oil Well ◽  
Application Domain ◽  
Well Drilling ◽  
Knowledge Intensive ◽  
Case Based

AbstractThis 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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