Inference, inquiry, evidence censorship, and explanation in connectionist expert systems

Abstract

The combination of the techniques of expert systems and neural networks has the potential of producing more powerful systems, for example, expert systems able to learn from experience. In this paper, we address the combinatorial neural model (CNM), a kind of fuzzy neural network able to accommodate in a simple framework the highly desirable property of incremental learning, as well as the usual capabilities of expert systems. We show how an interval-based representation for membership grades makes CNM capable of reasoning with several types of uncertainty: vagueness, ignorance, and relevance commonly found in practical applications. In addition, we show how basic functions of expert systems such as inference, inquiry, censorship of input information, and explanation may be implemented. We also report experimental results of the application of CNM to the problem of deforestation monitoring of the Amazon region using satellite images.