152,795 research outputs found
Inductive logic programming at 30
Inductive logic programming (ILP) is a form of logic-based machine learning.
The goal of ILP is to induce a hypothesis (a logic program) that generalises
given training examples and background knowledge. As ILP turns 30, we survey
recent work in the field. In this survey, we focus on (i) new meta-level search
methods, (ii) techniques for learning recursive programs that generalise from
few examples, (iii) new approaches for predicate invention, and (iv) the use of
different technologies, notably answer set programming and neural networks. We
conclude by discussing some of the current limitations of ILP and discuss
directions for future research.Comment: Extension of IJCAI20 survey paper. arXiv admin note: substantial text
overlap with arXiv:2002.11002, arXiv:2008.0791
Type-driven natural language analysis
The purpose of this thesis is in showing how recent developments in logic programming can be exploited to encode in a computational environment the features of certain linguistic theories. We are in this way able to make available for the purpose of natural language processing sophisticated capabilities of linguistic analysis directly justified by well developed grammatical frameworks.
More specifically, we exploit hypothetical reasoning, recently proposed as one of the possible directions to widen logic programming, to account for the syntax of filler-gap dependencies along the lines of linguistic theories such as Generalized Phrase Structure Grammar and Categorial Grammar. Moreover, we make use, for the purpose of semantic analysis of the same kind of phenomena, of another recently proposed extension, interestingly related to the previous one, namely the idea of replacing first-order terms with the more expressive λ-terms of λ-Calculus
Reasoning about Cardinal Directions between 3-Dimensional Extended Objects using Answer Set Programming
We propose a novel formal framework (called 3D-nCDC-ASP) to represent and
reason about cardinal directions between extended objects in 3-dimensional (3D)
space, using Answer Set Programming (ASP). 3D-nCDC-ASP extends Cardinal
Directional Calculus (CDC) with a new type of default constraints, and nCDC-ASP
to 3D. 3D-nCDC-ASP provides a flexible platform offering different types of
reasoning: Nonmonotonic reasoning with defaults, checking consistency of a set
of constraints on 3D cardinal directions between objects, explaining
inconsistencies, and inferring missing CDC relations. We prove the soundness of
3D-nCDC-ASP, and illustrate its usefulness with applications. This paper is
under consideration for acceptance in TPLP.Comment: Paper presented at the 36th International Conference on Logic
Programming (ICLP 2020), University Of Calabria, Rende (CS), Italy, September
2020, 29 pages, 6 figure
Programming models for sensor networks: a survey
Sensor networks have a significant potential in diverse applications some of which are already beginning to be deployed in areas such as environmental monitoring. As the application logic becomes more complex, programming difficulties are becoming a barrier to adoption of these networks. The difficulty in programming sensor networks is not only due to their inherently distributed nature but also the need for mechanisms to address their harsh operating conditions such as unreliable communications, faulty nodes, and extremely constrained resources. Researchers have proposed different programming models to overcome these difficulties with the ultimate goal of making programming easy while making full use of available resources. In this article, we first explore the requirements for programming models for sensor networks. Then we present a taxonomy of the programming models, classified according to the level of abstractions they provide. We present an evaluation of various programming models for their responsiveness to the requirements. Our results point to promising efforts in the area and a discussion of the future directions of research in this area.
Inductive logic programming at 30: a new introduction
Inductive logic programming (ILP) is a form of machine learning. The goal of
ILP is to induce a hypothesis (a set of logical rules) that generalises
training examples. As ILP turns 30, we provide a new introduction to the field.
We introduce the necessary logical notation and the main learning settings;
describe the building blocks of an ILP system; compare several systems on
several dimensions; describe four systems (Aleph, TILDE, ASPAL, and Metagol);
highlight key application areas; and, finally, summarise current limitations
and directions for future research.Comment: Paper under revie
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