Applying MLL to ILP

Abstract In Inductive Logic Programming (ILP), since logic is a complete (universal) language, in- finitely many,possible hypotheses,are compat- ible (hence plausible) given the evidence. An intrinsic way,of selecting the most,convenient hypothesis,from,the set of possible theories is not only useful for model,selection but it is also useful for guiding the search in the hypotheses space, as some ILP systems have done in the past. One selection/search criterion is to ap- ply Occam’s razor, i.e. to first select/try the simplest hypotheses,which,cover the evidence. In order to do this, it is necessary to mea- sure how,simple,a theory,is. The Minimum Message,Length (MML) principle is based,on information,theory,and,it reflects Occam’s ra- zor philosophy.,In this paper,we,present,a MML method,for costing both logic programs and,sets of facts according,to the theory. Our scheme,has a solid foundation,and,avoids the drawbacks of previous coding schemes in ILP, This work,has,been,partially,supported,by,the