Titlebook: Neural-Symbolic Learning and Reasoning; 18th International C Tarek R. Besold,Artur d’Avila Garcez,Benedikt Wagn Conference proceedings 2024

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Olga Mashkova,Fernando Zhapa-Camacho,Robert Hoehndorfhe specific computational problems.Focuses on a new language.The goal of this new edition is the same as for the first edition ”to?address the fault detection and isolation topics from a computational perspective“,?by covering the same important aspects, namely, (1) providing a completely general?th

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Context Helps: Integrating Context Information with?Videos in?a?Graph-Based HAR Frameworke-of-the-art (SoTA) models rely heavily on domain specific supervised fine-tuning of visual features, and even with this data- and compute-intensive fine-tuning, overall performance can still be limited. We argue that the next generation of HAR models could benefit from explicit neuro-symbolic mecha

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Variable Assignment Invariant Neural Networks for?Learning Logic Programssymbolic algorithms, but they are unable to deal with noise or generalize to unobserved transitions. Rule extraction based neural network methods suffer from overfitting, while more general implementation that categorize rules suffer from combinatorial explosion. In this paper, we introduce a techni

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ViPro: Enabling and?Controlling Video Prediction for?Complex Dynamical Scenarios Using Procedural Knh of data-driven models. On the basis of new challenging scenarios we show that state-of-the-art video predictors struggle in complex dynamical settings, and highlight that the introduction of prior process knowledge makes their learning problem feasible. Our approach results in the learning of a sy

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On the?Use of?Neurosymbolic AI for?Defending Against Cyber Attacksectionist and symbolic AI are currently being used to support such detection and response. In this paper, we make the case for combining them using neurosymbolic AI. We identify a set of challenges when using AI today and propose a set of neurosymbolic use cases we believe are both interesting resea