WiM : A Study On The Top-Down ILP Program Lubos Popelinsky, Olga Stepankova In the area of the inductive synthesis of logic programs it is the small number of examples which is crucial. We show that the classical MIS-like architecture can be adapted using techniques described in ILP literature so that we reach very good results if to compare with other ILP systems. We describe the top-down ILP program $WiM$ and the results obtained through it. WiM needs from 2 to 4 examples for most of the ILP benchmark predicates. Even though it is interactive, not more that one membership query is enough to receive the correct target program. WiM has higher efficiency of learning as well as smaller dependency on the quality of the example set in comparison to some of ILP programs. The quality of learning has been tested both on good examples and on randomly chosen example sets.