Journal of Computer Science

Prototyping Rule-Based Expert Systems with the Aid of Model Transformations

Alexander Yurievich Yurin, Nikita Olegovich Dorodnykh, Olga Anatolievna Nikolaychuk and Maksim Andreevich Grishenko

DOI : 10.3844/jcssp.2018.680.698

Journal of Computer Science

Volume 14, Issue 5

Pages 680-698

Abstract

The problem of improving efficiency of intelligence systems engineering remains a relevant topic of scientific research. One of the trends in this area is the use of the principles of cognitive (visual) modelling and design as well as approaches based on generative programming and model transformations. This paper aims to describe the implementation and application of model transformations for prototyping rule-based knowledge bases and expert systems. The implementation proposed uses the main principles of the Model Driven Architecture (MDA) (e.g., model types and creation stages) and considers the features of developing intelligent systems. Therefore, the current research employs the following tools: Ontologies for the representation of the computation-independent model; the author’s original notation, namely, the Rule Visual Modelling Language (RVML) to create the platform-independent and platform-specific models; the C Language Integrated Production System (CLIPS) and the Drools Rule Language (DRL) as the programming languages (as the platforms). The approach proposed targets non-programmers (domain experts and analytics) and makes the design process of rule-based expert systems and knowledge bases more efficient. The paper also presents a detailed description of the main elements of the approach including models, transformations and a specialised software (Personal Knowledge Base Designer).

Copyright

© 2018 Alexander Yurievich Yurin, Nikita Olegovich Dorodnykh, Olga Anatolievna Nikolaychuk and Maksim Andreevich Grishenko. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.