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Article
Scalable Query Answering Under Uncertainty to Neuroscientific Ontological Knowledge: The NeuroLang Approach
Researchers in neuroscience have a growing number of datasets available to study the brain, which is made possible by recent technological advances. Given the extent to which the brain has been studied, there ...
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Chapter
LNN: Logical Neural Networks
Logical Neural Networks (LNN) is a framework that assumes knowledge of a logic program a-priori and uses gradient descent to fit the logic program to training data via parameterized logical operators, resulting i...
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Chapter
Neuro Symbolic AI for Sequential Decision Making
Deep learning based approaches have been used to address several problems of a sequential nature, whether using supervised learning to learn a model of a time-series system or using reinforcement learning to t...
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Chapter
New Ideas in Neuro Symbolic Reasoning and Learning
Neuro symbolic reasoning and learning is a topic that combines ideas from deep neural networks with symbolic reasoning and learning to overcome several significant technical hurdles such as explainability, mod...
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Chapter
Neuro Symbolic Reasoning with Ontological Networks
In this chapter we describe neuro symbolic approaches developed for ontological domains, focusing mainly on Recurrent Reasoning Networks (RRNs), a formalism that was recently developed with the goal of automat...
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Chapter
Understanding SATNet: Constraint Learning and Symbol Grounding
The SATNet framework is a neural architecture designed to learn instances of combinatorial problems by learning the set of logical constraints associated with an instance of the maximum satisfiability problem....
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Chapter
Neuro Symbolic Applications
Although these days neural networks and deep learning get equated with AI, there are many systems that combine neural reasoning and learning with symbolic reasoning and learning modules. In the previous chapte...
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Chapter
LTN: Logic Tensor Networks
In this chapter, we provide an overview of Logic Tensor Networks (LTNs, for short), a formalism that makes use of tensor embeddings—n-dimensional vector representations—of elements tied to a logical syntax, which...
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Chapter
Neuro Symbolic Learning with Differentiable Inductive Logic Programming
In this chapter, we describe how a logic program can be learned from data in a neuro symbolic framework. Our focus is on the gradient-based method known as differentiable inductive logic programming (ILP), whi...
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Chapter
Fuzzy and Annotated Logic for Neuro Symbolic Artificial Intelligence
Various neuro symbolic approaches such as Logical Neural Networks, Logical Tensor Networks, differentiable ILP, and others rely on the use of several forms of real-valued logic and fuzzy operators. In this cha...
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Chapter
NeurASP
In this chapter, we explore a neuro symbolic approach to incorporate explicit knowledge about a domain while learning a model. Specifically, we use a differentiable extension of the declarative problem solving...
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Book
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Chapter
Brief Introduction to Propositional Logic and Predicate Calculus
Many recent neuro symbolic approaches rely on an underlying logical language. In this chapter, we provide a brief introduction to the basic concepts behind propositional logic and predicate calculus (first ord...
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Article
The Big-2/ROSe Model of Online Personality
The Big-5/OCEAN personality traits model, one of the central approaches to psychometrics, has been shown to have many applications over a variety of disciplines. In particular, correlations have been studied l...
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Article
NetDER: An Architecture for Reasoning About Malicious Behavior
Malicious behavior in social media has many faces, which for instance appear in the form of bots, sock puppets, creation and dissemination of fake news, Sybil attacks, and actors hiding behind multiple identit...
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Chapter
A Logic Programming Approach to Predict Enterprise-Targeted Cyberattacks
Although cybersecurity research has demonstrated that many of the recent cyberattacks targeting real-world organizations could have been avoided, proactively identifying and systematically understanding when and
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Chapter and Conference Paper
DAQAP: Defeasible Argumentation Query Answering Platform
In this paper we present the DAQAP, a Web platform for Defeasible Argumentation Query Answering, which offers a visual interface that facilitates the analysis of the argumentative process defined in the Defeasibl...
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Chapter
Explanation-Friendly Query Answering Under Uncertainty
Many tasks often regarded as requiring some form of intelligence to perform can be seen as instances of query answering over a semantically rich knowledge base. In this context, two of the main problems that a...
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Book
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Chapter
Baseline Cyber Attribution Models
Attributing the culprit of a cyberattack is widely considered one of the major technical and policy challenges of cybersecurity. While the lack of ground truth for an individual responsible for a given attack ...