Doctus is a Knowledge-Based Expert System and it consists of two major parts, the Knowledge Base and the Shell. Being a Shell means that Doctus is an empty software, designed to build the knowledge bases of the experts, which involves its systematization and, not rarely, discovery of new knowledge.

Building a knowledge base incorporates three processes: Knowledge Acquisition, Knowledge Engineering, which consist of systematization and fine tuning, and Application, all facilitated by Knowledge Engineer.

To represent knowledge Doctus uses symbolic logic, that is to say a formalism in which knowledge is expressed by logical statements consisting of symbols, namely self-defined terms of the expert (i.e. words) connected by “if…then” rules, also called production rules. Therefore Doctus belongs to domain of Symbolic Artificial Intelligence.

Knowledge-Based Systems are usually used to evaluate decision alternatives; therefore Doctus belongs to Decision Support Systems as well.

Decision alternatives in Doctus are called cases. The evaluation of cases is called reasoning. There are three types of reasoning in Doctus:

Original Decision

If the expert can articulate the important aspects of the decision as well as the rules, the system will trigger these rules to get the evaluation. This is called deduction or Rule-Based Reasoning. It is used when there is no experience in the domain, therefore the situation calls for Original Decision.

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If the knowledge engineering was successful, the knowledge-based system would propose the same evaluation of cases that the expert would. So what is added? The knowledge base is a transparent description of the knowledge of the expert (or group of experts), which is appropriate to argument the decision proposal easily.

Apart from the transparency the expert may discover new knowledge, realising that some attributes were irrelevant, or reshaping his knowledge by understanding the complex rules. This means, that some tacit relations between the explicit expectations of the expert became explicit.

Routine Decision

If the expert can articulate the aspects but he cannot say which of them are important and he cannot articulate the rules, though he is experienced enough (a few dozen cases with evaluation) this experience can be used to find out the rules describing the cases of his experience using induction, which is the symbolic version of Case-Based Reasoning. As there is extensive experience in the domain, the situation is described as Routine Decision.


The more obvious benefit of the case-based knowledge base is that the number of used attributes is reduced, to the informative ones. This makes the deputation of a decision much easier. Naturally, it is nothing of the sort of making programmed decision makers, as the Case-Based Graph represents the only the expert’s experience at given conditions. If a new case appear, which cannot be described with the knowledge base, it means there were no similar cases in the expert’s experience. The conditions may also change. Thus it is highly recommended to add the new cases constantly to the knowledge base, to maintain it as fresh as possible.

The greatest benefit of the building a case-based knowledge base is less obvious. This process is almost always accompanied with knowledge discovery, that is to say it makes a part of tacit knowledge explicit. It is very common that the expert is astonished at the first sight of the Case-Based Graph, thus the fine-tuning is not only necessary to make subtle adjustments to the knowledge base but also to get a deeper understanding of the result.


From the result of induction the important aspects of the decision can be determined using reduction. This is the third type of reasoning, though as it can only follow the induction, there is no third type of knowledge base, only two sorts of knowledge bases are built: rule-based knowledge base and case-based knowledge base.


The great benefit of the Case-Based Rule Reasoning is the reduced size, i.e. the significantly decreased number of the attributes. It enables the user to make a quick evaluation of new cases but attention is to be paid to possible loss of actuality. To avoid the use of outdated knowledge base, the original case-based knowledge base is to be maintained, constantly adding the new cases and regenerating the Case-Based Graph. If the conditions are changed, the Case-Based Graph will alter.

Download an evaluation copy: Doctus 3.0