Knowledge, acquisition

The knowledge base of an expert system contains the relevant knowledge about the specific area of the expert system. For this all necessary heuristics must be 

The final aim is to construct a formalized representation of the decision process. Decision trees and structured system analysis are possibilities. Some types of expert systems can derive their own rules from examples. These are described in Chapters 18 and 33. 

---

Construction of expert systems is facilitated if it is possible (at least approximately) to describe (model) expected signal from defect and non-defect pieces. If no models for the problem are available then the knowledge about the problem has to be acquired from an expert (the NDT inspector). However, the knowledge possessed by the expert is often incomplete and not well formalised, which makes knowledge acquisition a difficult task for the knowledge engineer. 

The whole knowledge acquisition process is also a burden for the inspection company, especially a smaller one, as it prevents the expert from taking part in routine inspection tasks. 

Apart from the cost of knowledge acquisition, another disadvantage of rule-based systems is the difficulty of rule-base maintenance. Rule-base maintenance may be required when changes are made to the inspection system, the inspection procedures, or if differing constructions are inspected. The maintenance usually cannot be done by end-users. 

The recognition ratios achieved by CBR systems developed as part of this project could not be bettered by either neural-network classifiers or rule-based expert system classifiers. In addition, CBR systems should be mote reliable than simple classifiers as they are programmed to recognise unknown data. The knowledge acquisition necessary to build CBR systems is less expensive than for expert systems, because it is simpler to describe the knowledge how to distinguish between certain types of data than to describe the whole data contents. 

The knowledge acquisition process outlined in Section 1.4 requires certain tasks to be performed. 

Reaction classification is an essential step in knowledge acquisition from reaction databases. 

L. Chen, Reaction classification and knowledge acquisition, in Handbook of Chemoinfyrmatics - From Data to Knowledge, ]. Gasteiger (Ed.), Wiley-VCH, Weinheim, 2003. 

The classical architecture of an expert system comprises a knowledge base, an inference engine, and some kind of user interface. Most expert systems also include an explanation subsystem and a knowledge acquisition subsystem. This architecture is given in Figure 9-34 and described in more detail below. 

Besides these three major components, many expert systems also comprise an explanation subsystem and a knowledge acquisition subsystem,... 

Knowledge acquisition subsystem The task of the knowledge acquisition subsystems is to assemble and upgrade the knowledge base. A major task is to verify the data and check for consistency. 

Testing knowledge acquisition interviews and/or the discussion of case studies can be conducted on a fairly informal, non-threatening basis. Whilst exams, open-book tests and multiple-choice tests are an option, they appear far too stringent within a mentoring context. 

Improvement of Data and Knowledge Acquisition and Knowledge Implementation Methods... 

Comas J, Llorens E, Marti E, Puig MA, Riera JL, Sabater F, Poch M (2003) Knowledge acquisition in the STREAMES Project the Key Process in the Environmental Decision Support System development. A1 Commun 16(4) 253-265... 

Rodriguez-Roda I, Comas J, Poch M, Sanchez-Marre M, Cortes U (2001) Automatic knowledge acquisition from complex processes for the development of knowledge-based systems. Ind Eng Chem Res 40(15) 3353-3360... 

Gaines, B., The trade-off between knowledge and data in knowledge acquisition. In Knowledge Discovery in Databases (G. Shapiro and W. Frawley, eds.), p. 491. MIT Press, Cambridge, MA, 1991. 

A. Hart, Knowledge Acquisition for Expert Systems. Kogan Page, London, 1986. 

In Chapter 43 the incorporation of expertise and experience in data analysis by means of expert systems is described. The knowledge acquisition bottleneck and the brittleness of domain expertise are, however, the major drawbacks in the development of expert systems. This has stimulated research on alternative techniques. Artificial neural networks (ANN) were first developed as a model of the human brain structure. The computerized version turned out to be suitable for performing tasks that are considered to be difficult to solve by classical techniques. 

Shells greatly simplify the programming of an ES, but they do not directly diminish the effort required in knowledge acquisition, which is the key task in its development. During the knowledge acquisition process, the relevant information is collated so that it can be inserted into the ES. This process obviously requires a human expert and, in addition, frequently needs the... 

---