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1

Rule based networks : an efficient and interpretable representation of computational models

Liu H., Gegov A., Cocea M.

Journal of Artificial Intelligence and Soft Computing Research

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2017

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Vol. 7, No. 2

111--123

EN

Due to the vast and rapid increase in the size of data, data mining has been an increasingly important tool for the purpose of knowledge discovery to prevent the presence of rich data but poor knowledge. In this context, machine learning can be seen as a powerful approach to achieve intelligent data mining. In practice, machine learning is also an intelligent approach for predictive modelling. Rule learning methods, a special type of machine learning methods, can be used to build a rule based system as a special type of expert systems for both knowledge discovery and predictive modelling. A rule based system may be represented through different structures. The techniques for representing rules are known as rule representation, which is significant for knowledge discovery in relation to the interpretability of the model, as well as for predictive modelling with regard to efficiency in predicting unseen instances. This paper justifies the significance of rule representation and presents several existing representation techniques. Two types of novel networked topologies for rule representation are developed against existing techniques. This paper also includes complexity analysis of the networked topologies in order to show their advantages comparing with the existing techniques in terms of model interpretability and computational efficiency.

2

A critical overview of methods for rules interoperability. Position paper

Kaczor K., Nalepa G.J.

Automatyka / Automatics

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2013

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Vol. 17, no. 1

53--58

EN

Rules are commonly used as a declarative method for knowledge representation. In the past, they were used in Rule-Based expert systems. Today they found application in such technologies as Complex Event Processing (CEP), Business Rules (BR), Business Processes Model and Notation and Semantic Web (SW). Despite their maturity, many unsolved problems still exist. One of them is related with rules interoperability and knowledge interchange. During the years many research were conducted and several methods were developed. Nevertheless, these methods suffer from their general and complex nature, which causes that the practical tool support is very weak.

PL

Reguły są powszechnie stosowana metoda deklaratywnej reprezentacji wiedzy. Dawniej wykorzystywane w ekspertowych systemach regułowych, dzisiaj znajdują nowe zastosowania. Pomimo swojej dojrzałości wciąż aktualny jest problem wymiany wiedzy pomiędzy różnymi reprezentacjami i formatami reguł. Istniejące technologie nie dostarczają efektywnych metod, a ponadto zawierają skomplikowane modele reprezentacji wiedzy, co skutkuje brakiem wsparcia narzędziowego. Niniejszy artykuł prezentuje obecny stan badań w obszarze wymiany wiedzy regułowej.

3

Od Drzew Tablicowych do Sieciowych Tablic Decyzyjnych: ewolucja zmodularyzowanych reprezentacji wiedzy

Wojnicki I.

Pomiary Automatyka Robotyka

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2011

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R. 15, nr 12

173-177

PL

Stosując systemy regułowe, można wyróżnić dwie klasy problemów: efektywność procesu projektowania oraz wydajność procesu uruchamiania. Niniejszy artykuł przeglądowo opisuje metody projektowania reguł opracowane w Katedrze Automatyki AGH pod kątem rozwiązywania ww. problemów. Proponowane podejścia opierają się w dużej mierze na modularyzacji bazy wiedzy i odpowiedniej jej wizualizacji. Przedstawiono ich ewolucję, jak również wyniki najnowszych badań dotyczących wnioskowania kontekstowego.

EN

There are two common issues while dealing with rules and rule-based systems. These are efficiency of the design process and performance of rule interpretation. This paper discusses briefly several design approaches developed at the Department of Automatics, AGH which tackle these issues. The proposed solutions are mainly based on modularization and appropriate visualization of the knowledge base being designed. Evolution of selected approaches and results of recent research regarding application of context-based reasoning are presented as well.