ASSESSMENT OF BOOLEAN MINIMIZATION IN SYMBOLIC EMPIRICAL LEARNING

We report research on the assessment of Boolean minimization in symbol ic empirical learning. We view training examples as logical expression s and implement Boolean Minimization (BM) heuristics to optimize input and to learn symbolic knowledge rules. We base our work on a BM learn ing system called BML. BML includes three components: a preprocessing, a BM, and a postprocessing component. The system incorporates Espress o-II, a popular system in very large scale integration design. The pre processing and postprocessing components include utilities that suppor t preparation of training examples.