Inductive Logic Programming (ILP) is the area of AI which deals with the induction of hypothesised predicate deenitions from examples and background knowledge. Logic programs are used as a single representation for examples, background knowledge and hypotheses. ILP is diierentiated from most other forms of Machine Learning (ML) both by its use of an expressive representation language and its ab...

We have proposed an extension to the deenition of general integer linear programs (ILP) to accept dis-equality constraints explicitly. A new class of logical variables is introduced to transform the extended ILP in general form to standard form. Branch and Bound algorithm is modiied to solve this new class of ILP.

Abstract Inductive logic programming (ILP) is a form of logic-based machine learning. The goal to induce hypothesis (a program) that generalises given training examples and background knowledge. As ILP turns 30, we review the last decade research. We focus on (i) new meta-level search methods, (ii) techniques for learning recursive programs, (iii) approaches predicate invention, (iv) use differ...

Since its introduction in the late 1950s, isolated limb perfusion (ILP) has been the preferred treatment option for locally advanced melanoma and sarcoma confined to a limb. This treatment results in high response rates with a satisfying duration of response in both tumor types. A drawback of ILP, however, is the invasive and complex character of the procedure. Isolated limb infusion (ILI) has ...

This chapter aims at demonstrating that inductive logic programming (ILP), a recently established subfield of machine learning that induces first-order clausal theories from examples, combines very well with the area of deductive databases. In the context of deductive databases, induction can be defined as inference of intensional knowledge from extensional knowledge. Classification-oriented IL...