## 4 Materials and Methods  
### Knowledge Acquisition and Representation  
There are various sources of knowledge to acquire such knowledge, such as the world wide web, databases, atlases, etc., but the main source of diagnostic knowledge is the experts in the field. Knowledge elicitation from experts for the creation of expert systems is a difficult process and is a bottleneck in the development process of such systems. Experts are not always able to convey this knowledge through verbal descriptions because it is not easy to put into words something that has become their experience and acts most of the time automatically, unconsciously. To overcome this difficulty, various methods are used to obtain this knowledge, such as interviews, observation, questionnaires, charts, etc. In our case, we used the interview and decision tree methods to capture the knowledge of the experts involved in the project. Several interviews were conducted with all four fish pathologists, who were instructed how to record the European sea bass disease diagnosis procedures in decision trees, upon which subsequent successive interviews were conducted, in order to arrive at the final trees, which were converted to production rule sets in the expert disease diagnosis system. There were various problems, which were overcome. One of them was the adaptation of each side (engineers, ichthyologists) to the technical terminology of the other, but also of the ichthyologists among themselves. There was difficulty in understanding the decision trees by ichthyologists, which was gradually overcome. Figure 1 illustrates the process followed for knowledge acquisition from fish pathologists. Figure 1: Ichthyological Knowledge Elicitation Process. First, the European sea bass diseases were recorded. These diseases are shown in Table 1. The list is not exhaustive but includes a major part of sea bass diseases. Also, metabolic diseases were not analyzed further because such an analysis was not considered necessary. Then, the parameters and symptoms related to the diagnosis of each sea bass disease were recorded. Then, the process of creating the decision trees for each disease began. Project Fish Health Specialists (FHSs) were given standard decision tree diagrams and explained how to design and operate them. The first diagrams had difficulties. It was observed that the semantics of the decision trees had not been understood by FHSs, and therefore several sessions of correcting the trees took place. The systematic effort to record the reasoning dunes as well as a re-evaluation of the parameters/symptoms recorded. This is illustrated in Figure 1 by the feedback to step 2 from step 3. In Table 2, the parameters and the symptoms related to the disease Myxobacteriosis are presented as an example. Also, after each change to a decision tree, there was a validation check of the tree with itself but also in relation to other related trees, which usually led to changes in the previous design of the tree; this was repeated until we arrived at a fully acceptable tree. This iterative process is captured in the flowchart in Figure 1 by the decision element after step 4 and the feedback arrow to step 3. Figure 2 shows an example of a decision tree, that of Myxobacteriosis. \begin{table}
\begin{tabular}{c c c} \hline
**No** & **Disease** & **Category** \\ \hline
1 & Aeromonas disease & Bacterial \\
2 & Mycobacteriaosis & Bacterial \\
3 & Myxobacteriosis & Bacterial \\
4 & Photobacteriaosis & Bacterial \\
5 & Vibriois (_Vibrio angullarum_) & Bacterial \\
6 & Vibriois (_Vibrio harveyi_) & Bacterial \\
7 & Caligus & Parasitic \\
8 & Ceratotho & Parasitic \\
9 & Diplectanum & Parasitic \\
10 & Lernanthropus & Parasitic \\
11 & VNN & Viral \\
12 & Metabolic Diseases & Metabolic \\ \hline \end{tabular}
\end{table} 
Table 1: Seabass diseases elicited from experts.