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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Towards Understanding the Impact of Graph Structure on Knowledge Graph Embeddingsthodologies for producing KGs, which span notions of expressivity, and are tailored for different use-cases and domains. Now, as neurosymbolic methods rise in prominence, it is important to understand how the development of KGs according to these methodologies impact downstream tasks, such as link p

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Metacognitive AI: Framework and the Case for a Neurosymbolic Approachgy. In this position paper, we examine the concept of applying metacognition to artificial intelligence. We introduce a framework for understanding metacognitive artificial intelligence (AI) that we call TRAP: transparency, reasoning, adaptation, and perception. We discuss each of these aspects in-t

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Enhancing Logical Tensor Networks: Integrating Uninorm-Based Fuzzy Operators for Complex Reasoning between t-norms and t-conorms, offer unparalleled flexibility and adaptability, making them ideal for modeling the complex, often ambiguous relationships inherent in real-world data. By embedding these operators into Logic Tensor Networks, we present a methodology that significantly increases the n

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Parameter Learning Using Approximate Model Counting these hybrid models, these methods use a knowledge compiler to turn the symbolic model into a differentiable arithmetic circuit, after which gradient descent can be performed. However, these methods require compiling a reasonably sized circuit, which is not always possible, as for many symbolic pro

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Large-Scale Knowledge Integration for Enhanced Molecular Property Predictionitical for advancements in drug discovery and materials science. While recent work has primarily focused on data-driven approaches, the KANO model introduces a novel paradigm by incorporating knowledge-enhanced pre-training. In this work, we expand upon KANO by integrating the large-scale ChEBI know