Neurosymbolic Artificial Intelligence

Abstract

In this position paper, we examine some of the assumptions held about logic and its relevance to the development of modern artificial intelligence (AI), which is primarily driven by deep learning. The paper aims to address fundamental misunderstandings ...

Abstract

Recently, there has been significant progress in the development of robust and highly scalable neurosymbolic description logic reasoners. However, the field faces challenges arising from diverse design strategies and evaluation methods. We address the ...

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Knowledge graphs (KGs) feature ever more frequently as symbolic components in neurosymbolic research and systems. But even though a central concern of neurosymbolic artificial intelligence is to combine neural learning with symbolic reasoning, relatively ...

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The field of knowledge engineering is experiencing a substantial impact from the rapid growth and widespread adoption of Neurosymbolic Systems (NeSys). In this paper, we investigate how NeSys are already used in knowledge engineering practices leading to ...

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Neural-Symbolic Integration (NSI) aims to marry the principles of symbolic AI techniques, such as logical reasoning, with the learning capabilities of neural networks. In recent years, many systems have been proposed to address this integration in a ...

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The paper surveys ongoing research on hyperdimensional computing and vector symbolic architectures which represent an alternative approach to neural computing with various advantages and interesting specific properties: transparency, error tolerance, ...

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This position paper discusses relationships among hybrid neural-symbolic models, dual-process theories, and cognitive architectures. It provides some historical backgrounds and argues that dual-process (implicit versus explicit) theories have significant ...

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Embedding based Knowledge Graph (KG) completion has gained much attention over the past few years. Most of the current algorithms consider a KG as a multidirectional labeled graph and lack the ability to capture the semantics underlying the schematic ...

Abstract

Deep learning is being very successful in supporting humans in the interpretation of complex data (such as images and text) for critical decision tasks. However, it still remains difficult for human experts to understand how such results are achieved, due ...

Abstract

Deep Deductive Reasoning refers to the training and then executing of deep learning systems to perform deductive reasoning in the sense of formal, mathematical logic. We discuss why this is an interesting and relevant problem to study, and explore how ...