As our dependence on intelligent machines continues to grow, so does the demand for more
transparent and interpretable models. In addition, the ability to explain the model generally …

The challenge of crafting intelligible intelligence Page 1 70 COMMUNICATIONS OF THE
ACM | JUNE 2019 | VOL. 62 | NO. 6 review articles ARTIFICIAL INTELLIGENCE (AI) systems …

Abstract Neuro-Symbolic Artificial Intelligence–the combination of symbolic methods with
methods that are based on artificial neural networks–has a long-standing history. In this …

Abstract Symbolic Systems in Artificial Intelligence which are based on formal logic and
deductive reasoning are fundamentally different from Artificial Intelligence systems based on …

In the last decade, ontologies have become widely adopted in a variety of fields ranging
from biomedicine, to finance, engineering, law, and cultural heritage. The ontology …

This survey explores the integration of learning and reasoning in two different fields of
artificial intelligence: neurosymbolic and statistical relational artificial intelligence …

Understanding why machine learning algorithms may fail is usually the task of the human
expert that uses domain knowledge and contextual information to discover systematic …

Abstract We propose Knowledge Enhanced Neural Networks (KENN), an architecture for
injecting prior knowledge, codified by a set of logical clauses, into a neural network. In …

In the recent past, there has been a growing interest in Neural-Symbolic Integration
frameworks, ie, hybrid systems that integrate connectionist and symbolic approaches to …

A long-standing ambition in artificial intelligence is to integrate predictors' inductive features
(ie, learning from examples) with deductive capabilities (ie, drawing inferences from …