Exact learning of p-dnf formulas with malicious membership queries

We consider exact learning of concepts using two types of query: extended equivalence queries, and malicious membership queries, that is, membership queries that are permitted to make errors on some arbitrarily chosen set of examples of a bounded cardinality. We present a randomized algorithm to learn-DNF formulas using these queries. The expected running time of the algorithm is polynomial in the number of variables and the maximum number of strings on which the membership oracle is allowed to make errors.