Inductive Logic Programming (ILP) systems construct models for data using domain-specific background information. When using these systems, it is typically assumed that sufficient human expertise is at hand to rule out irrelevant background information. Such irrelevant information can, and typically does, hinder an ILP system’s search for good models. Here, we provide evidence that if expertise...

The task of discovering interesting regularities in (large) sets of data (data mining, knowledge discovery) has recently met with increased interest in Machine Learning in general and in Inductive Logic Programming (ILP) in particular. However, while there is a widely accepted deenition for the task of concept learning from examples in ILP, deenitions for the data mining task have been proposed...

Generating genomic resources in terms of molecular markers is imperative in molecular breeding for crop improvement. Though development and application of microsatellite markers in large-scale was reported in the model crop foxtail millet, no such large-scale study was conducted for intron-length polymorphic (ILP) markers. Considering this, we developed 5123 ILP markers, of which 4049 were phys...

Inductive logic programming (ILP) techniques are useful for analyzing data in multi-table relational databases. Learned rules can potentially discover relationships that are not obvious in "flattened" data. Statistical learners, on the other hand, are generally not constructed to search relational data; they expect to be presented with a single table containing a set of feature candidates. Howe...

In the paper we consider the quadratic assignment problem arising from channel coding in communications where one coefficient matrix is the adjacency matrix of a hypercube in a finite dimensional space. By using the geometric structure of the hypercube, we first show that there exist at least n different optimal solutions to the underlying QAPs. Moreover, the inherent symmetries in the associat...

Image processing is widely used in many applications, including medical imaging, industrial manufacturing and security systems. In these applications, the size of the image is often very large, the processing time should be very small and the real-time constraints should be met. Therefore, during the last decades, there has been an increasing demand to exploit parallelism in applications. It is...

Inductive Logic Programming (ILP) is a promising technology for knowledge extraction applications. ILP has produced intelligible solutions for a wide variety of domains where it has been applied. The ILP lack of efficiency is, however, a major impediment for its scalability to applications requiring large amounts of data. In this paper we propose a set of techniques that improve ILP systems eff...

Current microprocessors exploit high levels of instruction-level parallelism (ILP) through techniques such as multiple issue, dynamic scheduling, and non-blocking reads. This paper presents the rst detailed analysis of the impact of such processors on shared-memory multiprocessors using a detailed execution-driven simulator. Using this analysis, we also examine the validity of common direct-exe...

Multi-robot task assignment is one of the main processes in an intelligent warehouse. This paper models multi-robot warehouse as open-path multi-depot asymmetric traveling salesman problem (OP-MATSP). A two-objective integer linear programming (ILP) model for solving OP-MDTSP proposed. The theoretical bound on computational time complexity this O(n!). We can solve small by ILP using Gurobi solv...