<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p>This paper describes a new approach, the HeKatE methodology, to the design and development of complex rule-based systems for control and decision support. The main paradigm for rule representation, namely, eXtended Tabular Trees (XTT), ensures high density and transparency of visual knowledge representation. Contrary to traditional, flat rule-based systems, the XTT approach is focused on groups of similar rules rather than on single rules. Such groups form decision tables which are connected into a network for inference. Efficient inference is assured as only the rules necessary for achieving the goal, identified by the context of inference and partial order among tables, are fired. In the paper a new version of the language—XTT2<sup>2</sup>—is presented. It is based on ALSV(FD) logic, also described in the paper. Another distinctive feature of the presented approach is a top-down design methodology based on successive refinement of the project. It starts with Attribute Relationship Diagram (ARD) development. Such a diagram represents relationships between system variables. Based on the ARD scheme, XTT tables and links between them are generated. The tables are filled with expert-provided constraints on values of the attributes. The code for rule representation is generated in a humanreadable representation called HMR and interpreted with a provided inference engine called HeaRT. A set of software tools supporting the visual design and development stages is described in brief.</p></abstract>

This paper describes a new approach, the HeKatE methodology, to the design and development of complex rule-based systems for control and decision support. The main paradigm for rule representation, namely, eXtended Tabular Trees (XTT), ensures high density and transparency of visual knowledge representation. Contrary to traditional, flat rule-based systems, the XTT approach is focused on groups of similar rules rather than on single rules. Such groups form decision tables which are connected into a network for inference. Efficient inference is assured as only the rules necessary for achieving the goal, identified by the context of inference and partial order among tables, are fired. In the paper a new version of the language—XTT22—is presented. It is based on ALSV(FD) logic, also described in the paper. Another distinctive feature of the presented approach is a top-down design methodology based on successive refinement of the project. It starts with Attribute Relationship Diagram (ARD) development. Such a diagram represents relationships between system variables. Based on the ARD scheme, XTT tables and links between them are generated. The tables are filled with expert-provided constraints on values of the attributes. The code for rule representation is generated in a humanreadable representation called HMR and interpreted with a provided inference engine called HeaRT. A set of software tools supporting the visual design and development stages is described in brief.

The HeKatE methodology. Hybrid engineering of intelligent systems

Department of Automatics, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059, Kraków, Poland

International Journal of Applied Mathematics and Computer Science

{"article-title":"The HeKatE methodology. Hybrid engineering of intelligent systems"}

This paper describes a new approach, the HeKatE methodology, to the design and development of complex rule-based systems for control and decision support. The...

The HeKatE methodology. Hybrid engineering of intelligent systems

Online veröffentlicht: 25. März 2010

Seitenbereich: 35 - 53

DOI: https://doi.org/10.2478/v10006-010-0003-9

Schlüsselwörter
rule-based expert systems, intelligent control, system design

This content is open access.

The HeKatE methodology. Hybrid engineering of intelligent systems

Online veröffentlicht: 25. März 2010

Seitenbereich: 35 - 53

DOI: https://doi.org/10.2478/v10006-010-0003-9

Schlüsselwörterrule-based expert systems, intelligent control, system design

This content is open access.

Schlüsselwörter
rule-based expert systems, intelligent control, system design