Inductive Logic Programming (ILP) is a Machine Learning technique that has been quite successful in knowledge discovery for relational domains. ILP systems implemented in Prolog challenge the limits of Prolog systems due to heavy usage of resources such as database accesses and memory usage, and to very long execution times. The major reason to implement ILP systems in Prolog is that the infere...

Intron length polymorphisms (ILPs) have been used as genetic markers in some studies. However, a systematic investigation and large-scale exploitation of ILP markers has not been reported. In this study, we performed a genome-wide search of ILPs between two subspecies (indica and japonica) in rice using the draft genomic sequences of cultivars 93-11 (indica) and Nipponbare (japonica) and 32,127...

A key problem in QSAR is the selection of appropriate descriptors to form accurate regression equations for the compounds under study. Inductive Logic Programming (ILP) algorithms are a class of machine learning algorithm that have been successfully applied to a number of SAR problems. Unlike other QSAR methods, which use attributes to describe chemical structure, ILP uses relations. This gives...

We present an ILP-based model of zero anaphora detection and resolution that builds on the joint determination of anaphoricity and coreference model proposed by Denis and Baldridge (2007), but revises it and extends it into a three-way ILP problem also incorporating subject detection. We show that this new model outperforms several baselines and competing models, as well as a direct translation...

Resource efficiency and survivability are critical concerns in elastic optical networks (EONs) with virtualization. In this paper, we investigate some important aspects network design against single-link failure under dedicated protection bandwidth squeezing schemes when multiple virtual topologies employed. We propose a link-path integer linear programming (ILP) formulation to solve the joint ...

We present stable ILP, a cross-disciplinary concept straddling machine learning and nonmonotonic reasoning. Stable models give meaning to logic programs containing negative assertions. In stable ILP, we employ stable models to represent the current state speciied by (possibly) negative EDB and IDB rules. The state then serves as the background knowledge for a top-down ILP learner. We present a ...

We present stable ILP, a cross-disciplinary concept straddling machine learning and nonmonotonic reasoning. Stable models give meaning to logic programs containing negative assertions. In stable ILP, we employ stable models to represent the current state speciied by (possibly) negative EDB and IDB rules. The state then serves as the background knowledge for a top-down ILP learner. We present a ...

With the increase in computing infrastructure commercialization through the pay-as-you-go model, competition among providers puts the user as a decision agent on which is the best provider to comply with his/her demands and requirements. Currently, users rely on instances o ered as on-demand, reserved, and spot to decide which is the best resource allocation model over the time. In this work, w...

The weighted constraint satisfaction problem (WCSP) occurs in the crux of many real-world applications of operations research, artificial intelligence, bioinformatics, etc. Despite its importance as a combinatorial substrate, many attempts for building an efficient WCSP solver have been largely unsatisfactory. In this paper, we introduce a new method for encoding a (Boolean) WCSP instance as an...

This paper proposes an iterative simulation optimisation approach to maximise the number of in-person consultations in blueprint schedule a clinic facing same-day multi-appointment patient trajectories and restrictions on patients simultaneously allowed waiting area, taking into account combined effects early arrival times (patients arriving from home), bridging (minimum time required between a...