Neuro-Symbolic Artificial Intelligence Current Trends

• URL: http://arxiv.org/abs/2105.05330v1
• Date: Tue, 11 May 2021 20:11:57 GMT
• Title: Neuro-Symbolic Artificial Intelligence Current Trends
• Authors: Md Kamruzzaman Sarker, Lu Zhou, Aaron Eberhart, Pascal Hitzler
• Abstract summary: We provide a structured overview of current trends, by means of categorizing recent publications from key conferences. The article is meant to serve as a convenient starting point for research on the general topic.
• Score: 7.329853242349448
• License: http://creativecommons.org/licenses/by/4.0/
• 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 article, we provide a structured overview of current trends, by means of categorizing recent publications from key conferences. The article is meant to serve as a convenient starting point for research on the general topic.

Related papers

• Mapping the Neuro-Symbolic AI Landscape by Architectures: A Handbook on Augmenting Deep Learning Through Symbolic Reasoning [11.418327158608664]
  Symbolic techniques with statistical strengths is a long-standing problem in artificial intelligence. Neuro-symbolic AI focuses on this integration where the methods are in particular neural networks. We present the first mapping of symbolic techniques into families of frameworks based on their architectures.
  arXiv  Detail & Related papers  (2024-10-29T14:35:59Z)
• Converging Paradigms: The Synergy of Symbolic and Connectionist AI in LLM-Empowered Autonomous Agents [55.63497537202751]
  Article explores the convergence of connectionist and symbolic artificial intelligence (AI) Traditionally, connectionist AI focuses on neural networks, while symbolic AI emphasizes symbolic representation and logic. Recent advancements in large language models (LLMs) highlight the potential of connectionist architectures in handling human language as a form of symbols.
  arXiv  Detail & Related papers  (2024-07-11T14:00:53Z)
• Complexity of Probabilistic Reasoning for Neurosymbolic Classification Techniques [6.775534755081169]
  We introduce a formalism for informed supervised classification and techniques. We then build upon this formalism to define three abstract neurosymbolic techniques based on probabilistic reasoning.
  arXiv  Detail & Related papers  (2024-04-12T11:31:37Z)
• Neurosymbolic AI and its Taxonomy: a survey [48.7576911714538]
  Neurosymbolic AI deals with models that combine symbolic processing, like classic AI, and neural networks. This survey investigates research papers in this area during recent years and brings classification and comparison between the presented models as well as applications.
  arXiv  Detail & Related papers  (2023-05-12T19:51:13Z)
• A Semantic Framework for Neuro-Symbolic Computing [0.36832029288386137]
  We provide a formal definition of semantic encoding, specifying the components and conditions under which a knowledge-base can be encoded. We show that many neuro-symbolic approaches are accounted for by this definition. This is expected to provide a guidance to future neuro-symbolic encodings by placing them in the broader context of the semantic encoding of entire families of existing neuro-symbolic systems.
  arXiv  Detail & Related papers  (2022-12-22T22:00:58Z)
• Constraints on the design of neuromorphic circuits set by the properties of neural population codes [61.15277741147157]
  In the brain, information is encoded, transmitted and used to inform behaviour. Neuromorphic circuits need to encode information in a way compatible to that used by populations of neuron in the brain.
  arXiv  Detail & Related papers  (2022-12-08T15:16:04Z)
• Towards Data-and Knowledge-Driven Artificial Intelligence: A Survey on Neuro-Symbolic Computing [73.0977635031713]
  Neural-symbolic computing (NeSy) has been an active research area of Artificial Intelligence (AI) for many years. NeSy shows promise of reconciling the advantages of reasoning and interpretability of symbolic representation and robust learning in neural networks.
  arXiv  Detail & Related papers  (2022-10-28T04:38:10Z)
• Survey on Applications of Neurosymbolic Artificial Intelligence [37.7665470475176]
  We introduce a taxonomy of common Neurosymbolic applications and summarize the state-of-the-art for each of those domains. We identify important current trends and provide new perspectives pertaining to the future of this burgeoning field.
  arXiv  Detail & Related papers  (2022-09-08T18:18:41Z)
• Neuromorphic Artificial Intelligence Systems [58.1806704582023]
  Modern AI systems, based on von Neumann architecture and classical neural networks, have a number of fundamental limitations in comparison with the brain. This article discusses such limitations and the ways they can be mitigated. It presents an overview of currently available neuromorphic AI projects in which these limitations are overcome.
  arXiv  Detail & Related papers  (2022-05-25T20:16:05Z)
• Neuro-Symbolic Learning of Answer Set Programs from Raw Data [54.56905063752427]
  Neuro-Symbolic AI aims to combine interpretability of symbolic techniques with the ability of deep learning to learn from raw data. We introduce Neuro-Symbolic Inductive Learner (NSIL), an approach that trains a general neural network to extract latent concepts from raw data. NSIL learns expressive knowledge, solves computationally complex problems, and achieves state-of-the-art performance in terms of accuracy and data efficiency.
  arXiv  Detail & Related papers  (2022-05-25T12:41:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.