Assessment of Boolean Minimization in Symbolic Empirical Learning

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