scholarly journals Features of Software Implementation of Logical Tasks in Prolog

2021 ◽  
pp. 116-120
Author(s):  
O.N. Polovikova ◽  
V.V. Shiryaev ◽  
N.M. Oskorbin ◽  
L.L. Smolyakova

One of the promising areas for using Prolog-systems is to solve logical tasks. This study outlines a solution approach based on the state generation procedure and the verification procedure. A solution to a logical task is presented, which demonstrates in practice the proposed approach and method of specifying a procedure for generating states. In the proposed example, a bit chain is generated that defines the code of a particular letter in the solution of the applied problem. Building a solution by means of code generation with verification allows not storing in the knowledge base a binary tree of all possible codes. The process of generating new states can be associated with the training of the program, with the dynamic formation of the knowledge base. The approach is based on the capabilities of software environments for adding facts and rules to existing ones, which were obtained as the results of the program or its stages. In this case, the entire program is the generating rule. An analysis of the constructed and tested procedures for the dynamic generation of states and the generation of facts allows us to talk about the applicability of such a solution for certain applied problems.

1993 ◽  
Vol 8 (1) ◽  
pp. 5-25 ◽  
Author(s):  
William Birmingham ◽  
Georg Klinker

AbstractIn the past decade, expert systems have been applied to a wide variety of application tasks. A central problem of expert system development and maintenance is the demand placed on knowledge engineers and domain experts. A commonly proposed solution is knowledge-acquisition tools. This paper reviews a class of knowledge-acquisition tools that presuppose the problem-solving method, as well as the structure of the knowledge base. These explicit problem-solving models are exploited by the tools during knowledge-acquisition, knowledge generalization, error checking and code generation.


Author(s):  
N. O. Dorodnykh ◽  
O. A. Nikolaychuk ◽  
A. Yu. Yurin

The paper is devoted to fuzzy knowledge base engineering problem. The effectiveness of this process can be improved by automated generation of source codes and analysis of data presented in different forms, in particular, in the form of conceptual models describing a certain subject domain. The knowledge base code generation is based on the transformation of conceptual models from the model-based approach and the use of metamodels. The metamodeling provides the description of the source and target formalisms of conceptual modeling and knowledge representation. We present an approach for fuzzy knowledge base engineering based on model transformations. In particular, metamodels for describing fuzzy rule-based models and fuzzy ontologies and method for automated metamodel generation are presented.


2020 ◽  
Vol 19 (9) ◽  
pp. 2221-2233 ◽  
Author(s):  
Margarita Gapeyenko ◽  
Andrey Samuylov ◽  
Mikhail Gerasimenko ◽  
Dmitri Moltchanov ◽  
Sarabjot Singh ◽  
...  

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