2.1 Components of an Expert Sys- tem
A typical expert system consists of four compo- nents: knowledge base, user interface, working memory and inference engine. The user interface acts as a pre-processing sys- tem that performs syntactic and semantic anal- ysis on user input. The relevant information is extracted and stored in the working memory.
The user interface corresponds to the sensory and responding system in the human cognition process. Ideally, it should involve understand- ing natural language and even machine percep- tion. However, the research on natural language 2.2 Knowledge Acquisition
Knowledge acquisition is a process which aims at extracting knowledge, experience and problem- solving procedures from one or more domain experts. There are many general and specific knowledge acquisition approaches available. A general knowledge acquisition approach consists of five stages as shown in Figure 1. The first stage, elicitation, aims at extract- ing domain experts’ knowledge about the prob- lem under consideration. Modelling is the pro- cess for transforming the findings of the first stage into more formal knowledge representa- tions. There is a feedback loop between these two stages that allows to make corrections of in- consistencies which might have caused by misun- derstanding between the expert and the knowl- edge engineer. Internal testing is verification process to ensure the correctness of the develop- ment before field trials can be proceeded. The field trial requires the involvement of all relevant parties to validate the developed product.
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The efficiency and effectiveness of the process of knowledge acquisition are affected by the fol- lowing [5]:
Experts may not be able to express their knowledge and experience in a structured way.
Experts may not be aware of the signifi- cance of the knowledge they have used.
Even experts can express their knowledge. it may be irrelevant, incomplete, incorrect and not understandable
Normally. domain expert are not necessary to fa- miliar with computers. In order to acquire their knowledge to meet the requirements of the devel- opment of expert systems. knowledge engineers are needed to bridge the gap between a computer system and domain experts. There are many methods used in knowledge acquisition and could be classified into 2 groups [lo], namely direct and indirect methods.
e Direct Approach: The knowledge engineer asks the expert to report on knowledge that he can directly articulate. The following methods are commonly used as direct ap- proach.
Interview Questionnaires - Observation of the task performance - Protocol analysis - Interruption analysis Drawing closed curves - Inference flow analysis
e Indirect Approach: The expert are not asked to express their knowledge directly but are asked some questions. By ask- ing these questions, other behaviour is col- lected. Some examples of indirect approach are as following:
- Multidimensional scaling - Johnson hierarchical clustering - Ordered tree from recall - Repertory grid analysis
The choice of the methods to be used depends on the domain field, the domain expert and the real sit>uation. It is not uncommon that more than one method are used to ensure full understand- ing of the real life situation. However, interview- ing the domain expert is frequently included as part of a knowledge acquisition process. Normally the knowledge acquisition is an iter- ative process which allows formulated knowledge to be verified and validated from time to time. In brief, the knowledge engineer extract knowl- edge from domain expert and then organise it in the form that can be utilised by an expert system. If the expert system can not provide satisfactory outcome, the domain expert will be consulted again for improvement. The amount of effort expended and the number of iterations required depend on the size of the system to be built, the depth and breath of the tasks to be supported, and the quality of the knowledge as it is acquired [9].
2.1 Components of an Expert Sys- temA typical expert system consists of four compo- nents: knowledge base, user interface, working memory and inference engine. The user interface acts as a pre-processing sys- tem that performs syntactic and semantic anal- ysis on user input. The relevant information is extracted and stored in the working memory. The user interface corresponds to the sensory and responding system in the human cognition process. Ideally, it should involve understand- ing natural language and even machine percep- tion. However, the research on natural language 2.2 Knowledge Acquisition Knowledge acquisition is a process which aims at extracting knowledge, experience and problem- solving procedures from one or more domain experts. There are many general and specific knowledge acquisition approaches available. A general knowledge acquisition approach consists of five stages as shown in Figure 1. The first stage, elicitation, aims at extract- ing domain experts’ knowledge about the prob- lem under consideration. Modelling is the pro- cess for transforming the findings of the first stage into more formal knowledge representa- tions. There is a feedback loop between these two stages that allows to make corrections of in- consistencies which might have caused by misun- derstanding between the expert and the knowl- edge engineer. Internal testing is verification process to ensure the correctness of the develop- ment before field trials can be proceeded. The field trial requires the involvement of all relevant parties to validate the developed product. 80 The efficiency and effectiveness of the process of knowledge acquisition are affected by the fol- lowing [5]: Experts may not be able to express their knowledge and experience in a structured way. Experts may not be aware of the signifi- cance of the knowledge they have used. Even experts can express their knowledge. it may be irrelevant, incomplete, incorrect and not understandable Normally. domain expert are not necessary to fa- miliar with computers. In order to acquire their knowledge to meet the requirements of the devel- opment of expert systems. knowledge engineers are needed to bridge the gap between a computer system and domain experts. There are many methods used in knowledge acquisition and could be classified into 2 groups [lo], namely direct and indirect methods. e Direct Approach: The knowledge engineer asks the expert to report on knowledge that he can directly articulate. The following methods are commonly used as direct ap- proach. Interview Questionnaires - Observation of the task performance - Protocol analysis - Interruption analysis Drawing closed curves - Inference flow analysis e Indirect Approach: The expert are not asked to express their knowledge directly but are asked some questions. By ask- ing these questions, other behaviour is col- lected. Some examples of indirect approach are as following: - Multidimensional scaling - Johnson hierarchical clustering - Ordered tree from recall - Repertory grid analysis The choice of the methods to be used depends on the domain field, the domain expert and the real sit>uation. It is not uncommon that more than one method are used to ensure full understand- ing of the real life situation. However, interview- ing the domain expert is frequently included as part of a knowledge acquisition process. Normally the knowledge acquisition is an iter- ative process which allows formulated knowledge to be verified and validated from time to time. In brief, the knowledge engineer extract knowl- edge from domain expert and then organise it in the form that can be utilised by an expert system. If the expert system can not provide satisfactory outcome, the domain expert will be consulted again for improvement. The amount of effort expended and the number of iterations required depend on the size of the system to be built, the depth and breath of the tasks to be supported, and the quality of the knowledge as it is acquired [9].
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