Joint inference approaches such as Integer Linear Programming (ILP) and Markov Logic Networks (MLNs) have recently been successfully applied to many natural language processing (NLP) tasks, often outperforming their pipeline counterparts. However, MLNs are arguably much less popular among NLP researchers than ILP. While NLP researchers who desire to employ these joint inference frameworks do no...

Integrated Layer Processing (ILP) has been presented as an implementation technique to improve communication protocol performance by reducing the number of memory references. Previous research has however not pointed out that in some circumstances ILP can significantly increase the number of memory references, resulting in lower communication throughput. We explore the performance effects of ap...

Inductive Logic Programming (ILP) systems have been largely applied to classification problems with a considerable success. The use of ILP systems in problems requiring numerical reasoning capabilities has been far less successful. Current systems have very limited numerical reasoning capabilities, which limits the range of domains where the ILP paradigm may be applied. This paper proposes impr...

We present a framework for the Induction of Functional Logic Programs (IFLP) from facts. This can be seen as an extension to the now consolidated field of Inductive Logic Programming (ILP). Inspired in the inverse resolution operator of ILP, we study the reversal of narrowing, the more usual operational mechanism for Functional Logic Programming. We also generalize the selection criteria for gu...

This paper presents an integer linear programming (ILP) technique to partition and schedule Synchronous Data Flow (SDF) graphs onto heterogeneous execution architectures. Our ILP formulation gives a partition and schedule for SDF graphs, which provide the optimal execution time. We incorporate new methods of exploiting stateless data-parallelism on a heterogeneous architecture. We quantitativel...

The insulin/insulin-like peptides (ILPs) regulate key events in physiology, including neural plasticity. However, the cellular and circuit mechanisms whereby ILPs regulate learning remain largely unknown. Here, we characterize two ILPs that play antagonistic roles in aversive olfactory learning of C. elegans. We show that the ILP ins-6 acts from ASI sensory neurons to enable learning by repress...

Inductive logic programming (ILP) is built on a foundation laid by research in machine learning and computational logic. Armed with this strong foundation, ILP has been applied to important and interesting problems in the life sciences, engineering and the arts. This paper begins by briefly reviewing some example applications, in order to illustrate the benefits of ILP. In turn, the application...

Open-ended learning is regarded as the ultimate milestone, especially in intelligent robotics. Preferably it should be unsupervised and it is by its nature inductive. In this article we want to give an overview of attempts to use Inductive Logic Programming (ILP) as a machine learning technique in the context of embodied autonomous agents. Relatively few such attempts exist altogether and the m...

Building a natural language interface for a database has been an interesting task since the 70‟s, which often requires creating a semantic parser. A study on using an advanced inductive logic programming (ILP) approach for semantic parser induction that combines different ILP learners to learn rules for ambiguity resolution is presented. Accuracy of the resulting induced semantic parser can be ...

The 3-partition problem admits a straightforward formulation as a 0-1 Integer Linear Program (ILP). We investigate problem instances for which the half-integer relaxation of the ILP is feasible, while the ILP is not. We prove that this only occurs on a set of at least 18 elements, and in case of 18 elements such an instance always contains an element of weight ≥ 10. These bounds are sharp: we g...