The inductive synthesis of recursive logic programs from incomplete information, such as input/output examples, is a challenging subfield both of ILP (Inductive Logic Programming) and of the synthesis (in general) of logic programs from formal specifications. We first overview past and present achievements, focusing on the techniques that were designed specifically for the inductive synthesis of recursive logic programs, but also discussing a few general ILP techniques that can also induce non-recursive hypotheses. Then we analyse the prospects of these techniques in this task, investigating their applicability to software engineering as well as to knowledge acquisition and discovery.

. Inductive logic programming (ILP) systems use some declarative bias to constrain the hypothesis space. We introduce a new declarative bias, called construction modes, capturing the required dataow of a relation, and design a language for expressing such construction modes. Their semantics is captured via the notion of admissibility. Experiments with the ILP systems synapse and dialogs have established the usefulness of construction modes. Since the new bias is orthogonal to the existing search biases, it can be used in conjunction with the existing biases. 1 Introduction In inductive logic programming (ILP) [10], a hypothesis H is to be inferred from assumed-to-be-incomplete information (or: evidence) E and background knowledge B such that B ^H j= E, where H , E, B are logic programs. Various methods are applied to constrain the hypothesis space. A relevant method is the provision of some declarative bias, which is any form of additional input information that restricts th...

The classical definition of the concept of least generalization of a clause set C is that it is a single clause. Since such a unique clause is sometimes over-general, we re-define this concept as being a minimal-sized set of clauses, each member of this set being the classical least generalization of some subset of C. The elements of these subsets are two by two compatible, in the sense that their classical least generalizations are not too general according to some over-generality criterion. We give an algorithm for computing this re-defined concept, providing thus a new approach to disjunctive concept learning. The criterion for over-generality is problem-specific. We introduce a new such criterion: two clauses are compatible, i.e., their classical least generalization is not over-general, if this least generalization is admissible wrt a construction mode capturing the required dataflow of each involved relation. We design a language for expressing such construction modes, ...