Recent years have seen a surge of interest in Probabilistic Logic Programming (PLP) and Statistical Relational Learning (SRL) models that combine logic with probabilities. Structure learning of these systems is an intersection area of Inductive Logic Programming (ILP) and statistical learning (SL). However, ILP cannot deal with probabilities, SL cannot model relational hypothesis. The biggest c...

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...

The Variable Precision Rough Set Inductive Logic Programming model (VPRSILP model) extends the Variable Precision Rough Set (VPRS) model to Inductive Logic Programming (ILP). This paper presents cVPRSILP, an approach based on the VPRSILP model, that uses attributes based on clauses of interest to define the elementary sets. An illustrative experiment using the Predictive Toxicology Evaluation C...

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.

We present a method for deriving minimal tests to cover multiple fault models. Integer linear programming (ILP) is used to select a minimum set of vectors without reducing the original coverage. Tests can be initially generated separately for different fault models. All tests are then simulated for each fault model of interest without fault dropping. The fault simulation data are converted into...

This book is a collective endeavor of exploring the role of technology in interlanguage pragmatics (ILP) research, instruction, and assessment. It starts with an introduction (Chapter 1) and ends with a commentary and a prologue, both of which envision future directions of using technology in the field of ILP. The included studies are divided into two parts: Part I (Chapters 2 to 6) which addre...

This paper focuses on unsupervised modeling of morphological families, collectively comprising a forest over the language vocabulary. This formulation enables us to capture edgewise properties reflecting single-step morphological derivations, along with global distributional properties of the entire forest. These global properties constrain the size of the affix set and encourage formation of t...