Functional decomposition of MVL functions using multi-valued decision diagrams

In this paper, the minimization of incompletely specified multi-valued functions using functional decomposition is discussed. From the aspect of machine learning, learning samples can be implemented as minterms in multi-valued logic. The representation, can then be decomposed into smaller blocks, resulting in a reduced problem complexity. This gives induced descriptions through structuring, or feature extraction, of a learning problem. Our approach to the decomposition is based on expressing a multi-valued function (learning problem) in terms of a multi-valued decision diagram that allows the use of Don't Cares. The inclusion of Don't Cares is the emphasis for this paper since multi-valued benchmarks are characterized as having many Don't Cares.