Common Sense Reasoning in Diagnostic Systems

Abstract

The problem of human reasoning modeling (so called “common sense” reasoning) in artificial intelligence systems and especially in intelligent decision support systems (IDSS) is very actual nowadays [Vagin et al., 2001]. That is why special attention is turned to casebased and analogous reasoning methods and models. The analogies and precedents (cases) can be used in various applications of artificial intelligence (AI) and for solving various problems, e.g., for diagnostics and forecasting or for machine learning. AI experts model case-based reasoning by computers in order to develop more flexible models of search for solutions and learning. Investigation of mechanisms that are involved in the analogous reasoning process is an important problem for the specialists in AI. The analogy can be used in various applications of AI and for solving various problems, e.g., for generation of hypotheses about an unknown problem domain or for generalizing experience in the form of an abstract scheme. The great interest in this problem is caused by the necessity of modeling human reasoning (common sense reasoning) in AI systems and, in particular, in IDSS of real time. Reasoning by analogy is to transfer of knowledge obtained from an object to a less studied one which is similar to the former with respect to some essential properties or attributes. Reasoning of this kind is a source of scientific hypotheses. Thus, analogy-based reasoning can be defined as a method that allows to understand a situation when compared with another one. In other words, an analogy is an inference method that allows to detect likeness between several given objects due to transfer of facts and knowledge valid for both objects, to other objects and to determine means of problem solution or to forecast unknown properties. Case-based reasoning, like reasoning by analogy, is based on analogy; however, there are certain differences in their implementation. In the most encyclopedias, a precedent (from Latin, precedentis) is defined as a case that took place earlier and is an example or justification for subsequent events of this kind. Creating a precedent is to give grounds for similar cases in the future, and establishing a precedent is to find a similar case in the past. The generalized structure of a real-time IDSS (RT IDSS) is represented in Fig. 1 [Vagin et al., 2007].