Application of goal tree-success tree model as the knowledge-base of operator advisory systems

Abstract

The most important portion of an expert system development is the articulation of knowledge by the expert and its satisfactory formulation in a suitable knowledge representation scheme for mechanization by a computer. A “deep knowledge” approach called Goal Tree-Success Tree model is devised to represent complex dynamic domain knowledge. This approach can hierarchically model the underlying principles of a given process domain (for example nuclear power plant operations domain). The Goal Tree-Success Tree can then be used to represent the knowledge-base and provide means of selecting an efficient search routine in the inference engine of an expert system. A prototype expert system has been developed to demonstrate the method. This expert system models the operation of a typical feedwater system used in the pressurized water reactors. The expert system is modeled for real-time operations if an interface between plant parameters and the expert system is established. The real-time operation provides an ability to quickly remedy minor disturbances that can quickly lead to a system malfunction or trip. A description of both the Goal Tree-Success Tree model and the prototype expert system is presented.