An Application of a Neural Net for Fuzzy Abductive Reasoning

This is a description of a simple system that is able of performing abductive reasoning over fuzzy data using a back-propagation neural net for the hypothesis generation process.I will first outline and exemplify the notion of abduction as a process of building hypotheses on the basis of a given set of data, evaluating them to find the best hypothesis and give explanation for the made selection. I extend this notion to account for abductive reasoning over fuzzy data. As an example I describe the classification of objects according to fuzzy sensory features into previously learned categories that were represented by a set of objects described by feature-value-pairs from which prototypes are detected which form the center of a category.In the following a brief description of the back-propagation algorithm and a design of a demonstration system that is capable of carrying out abductive reasoning in a small example domain is given. The system is able to learn to classify kinds of fruit given certain feature-value-pairs and to detect the most prototypical feature-value-pair-clusters within a category. The trained neural net is used for the hypothesis generation process. It also provides very critical information for the evaluation and explanation of hypotheses. I then discuss the implementation of an evaluation and explanation component using the specific capabilities of the neural net.

ICSI Technical Report TR-93-044

Technical Report