Along with the great success of deep neural networks, there is also growing concern about
their black-box nature. The interpretability issue affects people's trust on deep learning …

Neuro-symbolic AI is an emerging subfield of Artificial Intelligence that brings together two
hitherto distinct approaches.” Neuro” refers to the artificial neural networks prominent in …

An intelligent agent interacting with the real world will encounter individual people, courses,
test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of …

Rules–the clearest, most explored and best understood form of knowledge representation–
are particularly important for data mining, as they offer the best tradeoff between human and …

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Abstract Inductive Logic Programming (ILP) is a new discipline which investigates the
inductive construction of first-order clausal theories from examples and background …

Abstract Knowledge graph reasoning is the fundamental component to support machine
learning applications such as information extraction, information retrieval, and …

This paper firstly provides a re-appraisal of the development of techniques for inverting
deduction, secondly introduces Mode-Directed Inverse Entailment (MDIE) as a …

The problem of concept learning, or forming a general description of a class of objects given
a set of examples and non-examples, is viewed here as a search problem. Existing …

The Definitive Volume on Cutting-Edge Exploratory Analysis of Massive Spatial and
Spatiotemporal DatabasesSince the publication of the first edition of Geographic Data …