Expert systems constraint-based

Foulkes et al. (1988) have approached the synthesis of operating procedures from a more empirical angle. They have extended the work of Rivas and Rudd (1974) for the synthesis of complex pump and valve sequencing operations, relying on the use of logical propositions (implemented as rule-based expert systems), which capture the various types of constraints imposed on the states of a processing system. 

To meet these constraints, we chose for our development tool Insight 2+, a rule-based and microcomputer-based expert system shell that met most of the criteria of a good tool, as described by Waterman (2)—that is, it has good user interface, rule-tracing, and debugging capabilities. In addition, Insight 2+ has no requirements for licensing fees. 

The MidHAL module allocates a set of fiinctional units to perform the scheduled operations, but does not bind specific operations to specific fiinctional units. This allocation is done by a rule-based expert system, which takes into account the available cells, their area cost, and the timing constraints. 

Processing Characteristics of IQAP. To follow the experts methodology, it appears that the processing within IQAP will be performed in two steps. In the first step, the inputs should be accepted and a constraint-based system will develop a set of constraints and limits that apply to the data. The knowledge embedded in this first system will convert the constraints into an analytical method and the associated set of QC criteria. It is expected that because QA/QC Objectives incorporate considerations regarding the usability of the data, the constraints will be flexible enough to process the inputs into a reasonably structured set of data tables. Therefore, in the second step, the method and its associated QC criteria will be represented in a relational database. The level of detail in the specifications of the QC criteria and the relationships between the data elements will be more specific as the process proceeds from the first step to the second step. 

Because of cost and time constraints, we have not had the opportunity to actually work with and evaluate many of the tools mentioned. Consequently our conclusions are based in large measure on the comments received from those working in the fields of AI/Expert Systems and Software Verification and Validation. Also, my conclusions are those of a potential user rather than of a specialist in the field. 

As the reasoning of a human expert is goal-oriented and driven by the external situations, so the organization and the correct instantiation of the production rules inside a Knowledge Base depend on the system goal and on the external constraints. 

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