  We propose a query learning algorithm for ordered multi-terminal binary
decision diagrams (OMTBDDs) using at most n equivalence and 2n(l\lcei\log_2
m\rceil+ 3n) membership queries by extending the algorithm for ordered binary
decision diagrams (OBDDs). Tightness of our upper bounds is checked in our
experiments using synthetically generated target OMTBDDs. Possibility of
applying our algorithm to classification problems is also indicated in our
other experiments using datasets of UCI Machine Learning Repository.
