This report describes the design and the prototyping of a compiling tool for the automated implementation of distributed applications: ALFred. This compiler starts from the formal specification of an application written in ESTEREL, and then integrates end-to-end communication functions tailored to the application characteristics (described in the specification) and produces a high performance i...

This paper introduces a new Inductive Logic Programming (ILP) framework called Top Directed Hypothesis Derivation (TDHD). In this framework each hypothesised clause must be derivable from a given logic program called top theory (>). The top theory can be viewed as a declarative bias which defines the hypothesis space. This replaces the metalogical mode statements which are used in many ILP syst...

We use Inductive Logic Programming (ILP) within a chart-parsing framework for grammar learning. Given an existing grammar G, together with some sentences which G can not parse, we use ILP to nd the \miss-ing" grammar rules or lexical items. Our aim is to exploit the inductive capabilities of chart parsing, i.e. the ability to eeciently determine what is needed for a parse. For each unparsable s...

Isolated lung perfusion (ILP) has been historically used as a method to study basic lung physiologic concepts using animal models. More recently, ILP has been applied in lung transplantation and thoracic oncology. In lung transplantation, ILP has been used to assess physiological integrity of donor lungs after the organ is removed from the donor. This procedure is called Ex vivo Lung Perfusion ...

Inductive Logic Programming (ILP) systems construct explanations for data in terms of domain-speciic background information. How does the quality of this information aaect the performance of an ILP system? Results from experiments concerned with learning simple programs for list processing suggest that performance is sensitive to the type and amount of background knowledge provided. In particul...

The goal of inductive logic programming (ILP) is to search for a hypothesis that generalises training examples and background knowledge (BK). To improve performance, we introduce an approach that, before searching hypothesis, first discovers "where not search". We use given BK discover constraints on hypotheses, such as number cannot be both even odd. the bootstrap constraint-driven ILP system....

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...