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This paper describes a new approach, the HeKatE methodology, to the design and development of complex rule-basedsystems 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-basedsystems, the XTT approach is focused on groups of similar rules rather than on single rules. Such groups form decisiontables which are connected into a network for inference. ; Efficient inference is assured as only the rules necessary forachieving the goal, identified by the context of inference and partial order among tables, are fired. In the paper a newversion of the language-XTT2-is presented. It is based on ALSV(FD) logic, also described in the paper. Anotherdistinctive feature of the presented approach is a top-down design methodology based on successive refinement of theproject. It starts with Attribute Relationship Diagram (ARD) development. ; Such a diagram represents relationships betweensystem variables. Based on the ARD scheme, XTT tables and links between them are generated. The tables are filledwith expert-provided constraints on values of the attributes. The code for rule representation is generated in a humanreadablerepresentation called HMR and interpreted with a provided inference engine called HeaRT. A set of software toolssupporting the visual design and development stages is described in brief.