<P> Performance was especially problematic because early expert systems were built using tools such as Lisp, which executed interpreted (rather than compiled) code . Interpreting provided an extremely powerful development environment but with the drawback that it was virtually impossible to match the efficiency of the fastest compiled languages, such as C. System and database integration were difficult for early expert systems because the tools were mostly in languages and platforms that were neither familiar to nor welcome in most corporate IT environments--programming languages such as Lisp and Prolog, and hardware platforms such as Lisp machines and personal computers . As a result, much effort in the later stages of expert system tool development was focused on integrating with legacy environments such as COBOL and large database systems, and on porting to more standard platforms . These issues were resolved mainly by the client - server paradigm shift, as PCs were gradually accepted in the IT environment as a legitimate platform for serious business system development and as affordable minicomputer servers provided the processing power needed for AI applications . </P> <P> Hayes - Roth divides expert systems applications into 10 categories illustrated in the following table . The example applications were not in the original Hayes - Roth table, and some of them arose well afterward . Any application that is not footnoted is described in the Hayes - Roth book . Also, while these categories provide an intuitive framework to describe the space of expert systems applications, they are not rigid categories, and in some cases an application may show traits of more than one category . </P> <Table> <Tr> <Th> </Th> <Th> Problem addressed </Th> <Th> Examples </Th> </Tr> <Tr> <Td> Interpretation </Td> <Td> Inferring situation descriptions from sensor data </Td> <Td> Hearsay (speech recognition), PROSPECTOR </Td> </Tr> <Tr> <Td> Prediction </Td> <Td> Inferring likely consequences of given situations </Td> <Td> Preterm Birth Risk Assessment </Td> </Tr> <Tr> <Td> Diagnosis </Td> <Td> Inferring system malfunctions from observables </Td> <Td> CADUCEUS, MYCIN, PUFF, Mistral, Eydenet, Kaleidos </Td> </Tr> <Tr> <Td> Design </Td> <Td> Configuring objects under constraints </Td> <Td> Dendral, Mortgage Loan Advisor, R1 (DEC VAX Configuration), SID (DEC VAX 9000 CPU) </Td> </Tr> <Tr> <Td> Planning </Td> <Td> Designing actions </Td> <Td> Mission Planning for Autonomous Underwater Vehicle </Td> </Tr> <Tr> <Td> Monitoring </Td> <Td> Comparing observations to plan vulnerabilities </Td> <Td> REACTOR </Td> </Tr> <Tr> <Td> Debugging </Td> <Td> Providing incremental solutions for complex problems </Td> <Td> SAINT, MATHLAB, MACSYMA </Td> </Tr> <Tr> <Td> Repair </Td> <Td> Executing a plan to administer a prescribed remedy </Td> <Td> Toxic Spill Crisis Management </Td> </Tr> <Tr> <Td> Instruction </Td> <Td> Diagnosing, assessing, and repairing student behavior </Td> <Td> SMH. PAL, Intelligent Clinical Training, STEAMER </Td> </Tr> <Tr> <Td> Control </Td> <Td> Interpreting, predicting, repairing, and monitoring system behaviors </Td> <Td> Real Time Process Control, Space Shuttle Mission Control </Td> </Tr> </Table> <Tr> <Th> </Th> <Th> Problem addressed </Th> <Th> Examples </Th> </Tr>

Provide a link to the computerized expert system