scholarly journals An inferencing procedure for guaranteeing the search time of a production-rule knowledge base.

1986 ◽  
Author(s):  
Haleh Vafaie
2019 ◽  
Vol 8 (5) ◽  
Author(s):  
Timur N. Karimov ◽  
Shamil A. Khamadeev

: In this work the problem of various discrepancies knowledge management is considered during realization of technological production processes of polypropylene tubes. Such discrepancies can make serious impact on manufacturing efficiency enterprise for the reasons of the compelled equipment stand still, sharp decline in final product quality, failures to meet time constraints of production shipment for the consumer, etc. In order to remove discrepancies it is required to define quickly the major factors exerting negative impact on production quality. And for each type of a product there can be the set of factors. The main way for work with the large volume of information concerning problems, their factors and ways of elimination is experience of experts. Such way owing to a human factor is not reliable and cannot be considered as the effective solution of the considered problem. In article as the decision the structure of the knowledge base on the basis of precedents (use cases) is offered. The precedent represents the information block including a basic situation and the decision corresponding to it. The offered structure is founded on hierarchy to Isikava's chart, one of popular instruments of quality control, and listed products. For filling of base precedents it is offered to use an algorithm of a clustering of data CLOPE. Results of work are the three-level structure of the knowledge base, model of a precedent, model of processes of addition of a new precedent and search of a precedent in the knowledge base, an algorithm of a clustering of precedents. It was revealed that the preliminary clustering allows reducing search time considerably. This approach can be used at a stage of technological preparation of production


Algorithms ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 188
Author(s):  
Dan Yu ◽  
Peng Liu ◽  
Dezhi Qiao ◽  
Xianglong Tang

In view of the characteristics of the guidance, navigation and control (GNC) system of the lunar orbit rendezvous and docking (RVD), we design an auxiliary safety prediction system based on the human–machine collaboration framework. The system contains two parts, including the construction of the rendezvous and docking safety rule knowledge base by the use of machine learning methods, and the prediction of safety by the use of the base. First, in the ground semi-physical simulation test environment, feature extraction and matching are performed on the images taken by the navigation surveillance camera. Then, the matched features and the rendezvous and docking deviation are used to form training sample pairs, which are further used to construct the safety rule knowledge base by using the decision tree method. Finally, the safety rule knowledge base is used to predict the safety of the subsequent process of the rendezvous and docking based on the current images taken by the surveillance camera, and the probability of success is obtained. Semi-physical experiments on the ground show that the system can improve the level of intelligence in the flight control process and effectively assist ground flight controllers in data monitoring and mission decision-making.


2016 ◽  
Vol 32 (4) ◽  
pp. 298-306 ◽  
Author(s):  
Samuel Greiff ◽  
Katarina Krkovic ◽  
Jarkko Hautamäki

Abstract. In this study, we explored the network of relations between fluid reasoning, working memory, and the two dimensions of complex problem solving, rule knowledge and rule application. In doing so, we replicated the recent study by Bühner, Kröner, and Ziegler (2008) and the structural relations investigated therein [ Bühner, Kröner, & Ziegler, (2008) . Working memory, visual-spatial intelligence and their relationship to problem-solving. Intelligence, 36, 672–680]. However, in the present study, we used different assessment instruments by employing assessments of figural, numerical, and verbal fluid reasoning, an assessment of numerical working memory, and a complex problem solving assessment using the MicroDYN approach. In a sample of N = 2,029 Finnish sixth-grade students of which 328 students took the numerical working memory assessment, the findings diverged substantially from the results reported by Bühner et al. Importantly, in the present study, fluid reasoning was the main source of variation for rule knowledge and rule application, and working memory contributed only a little added value. Albeit generally in line with previously conducted research on the relation between complex problem solving and other cognitive abilities, these findings directly contrast the results of Bühner et al. (2008) who reported that only working memory was a source of variation in complex problem solving, whereas fluid reasoning was not. Explanations for the different patterns of results are sought, and implications for the use of assessment instruments and for research on interindividual differences in complex problem solving are discussed.


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