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Relevant bibliographies by topics / Neuro-symbolic networks

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Academic literature on the topic 'Neuro-symbolic networks'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Neuro-symbolic networks"

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Burattini, Ernesto, Antonio de Francesco, and Massimo de Gregorio. "NSL: A Neuro–Symbolic Language for a Neuro–Symbolic Processor (NSP)." International Journal of Neural Systems 13, no. 02 (2003): 93–101. http://dx.doi.org/10.1142/s0129065703001480.

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A Neuro–Symbolic Language for monotonic and non–monotonic parallel logical inference by means of artificial neural networks (ANNs) is presented. Both the language and its compiler have been designed and implemented in order to translate the neural representation of a given problem into a VHDL software, which in turn can set devices such as FPGA. The result of this operation leads to an electronic circuit that we call NSP (Neuro–Symbolic Processor).

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Fatima, Tuba, and Dr Rehan Muhammad. "The Impact of Neuro-Symbolic AI on Cognitive Linguistics." ACADEMIA International Journal for Social Sciences 4, no. 3 (2025): 455–66. https://doi.org/10.63056/acad.004.03.0386.

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Neuro-Symbolic Artificial Intelligence (AI) is indeed a fascinating domain, merging the structured reasoning of symbolic methods with the learning capabilities of neural networks. Its long-standing history reflects its significance in advancing AI towards achieving more robust and interpretable solutions. Neuro-symbolic AI is such an exciting and transformative field, as it combines the structured reasoning of symbolic AI with the adaptability and learning capabilities of neural networks. Your summary elegantly captures the breadth and depth of this growing discipline. The focus on representat

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Yevdokymov, Serhii. "Neuro-symbolic models for ensuring cybersecurity in critical cyber-physical systems." Computational Problems of Electrical Engineering 14, no. 1 (2024): 42–50. https://doi.org/10.23939/jcpee2024.01.042.

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This paper presents the results of a comprehensive study on the application of the neuro-symbolic approach for detecting and preventing cyber threats in railway systems, a critical component of cyber-physical infrastructures. The increasing complexity and integration of physical systems with digital technologies have made such infrastructures vulnerable to cyberattacks, where breaches can result in severe consequences, including system failures, financial losses, and threats to public safety and the environment. The objective of this study was to assess the effectiveness of the neuro-symbolic

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Anil Kumar. "Neuro Symbolic AI in personalized mental health therapy: Bridging cognitive science and computational psychiatry." World Journal of Advanced Research and Reviews 19, no. 2 (2023): 1663–79. https://doi.org/10.30574/wjarr.2023.19.2.1516.

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Personalized mental health therapy has gained increasing attention as advancements in artificial intelligence (AI) enable tailored treatment strategies based on individual cognitive and emotional profiles. Neuro-symbolic AI, a hybrid approach combining symbolic reasoning and neural networks, offers a promising solution for bridging cognitive science and computational psychiatry. Unlike conventional AI models that rely solely on deep learning, neuro-symbolic AI integrates human-interpretable knowledge representations with data-driven learning, enhancing the adaptability and explainability of AI

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Sen, Prithviraj, Breno W. S. R. de Carvalho, Ryan Riegel, and Alexander Gray. "Neuro-Symbolic Inductive Logic Programming with Logical Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (2022): 8212–19. http://dx.doi.org/10.1609/aaai.v36i8.20795.

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Recent work on neuro-symbolic inductive logic programming has led to promising approaches that can learn explanatory rules from noisy, real-world data. While some proposals approximate logical operators with differentiable operators from fuzzy or real-valued logic that are parameter-free thus diminishing their capacity to fit the data, other approaches are only loosely based on logic making it difficult to interpret the learned ``rules". In this paper, we propose learning rules with the recently proposed logical neural networks (LNN). Compared to others, LNNs offer a strong connection to class

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Morel, Gilles. "Neuro-symbolic A.I. for the smart city." Journal of Physics: Conference Series 2042, no. 1 (2021): 012018. http://dx.doi.org/10.1088/1742-6596/2042/1/012018.

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Abstract Smart building and smart city specialists agree that complex, innovative use cases, especially those using cross-domain and multi-source data, need to make use of Artificial Intelligence (AI). However, today’s AI mainly concerns machine learning and artificial neural networks (deep learning), whereas the first forty years of the discipline (the last decades of the 20th century) were essentially focused on a knowledge-based approach, which is still relevant today for some tasks. In this article we advocate a merging of these two AI trends – an approach known as neuro-symbolic AI – for

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Marra, Giuseppe. "From Statistical Relational to Neuro-Symbolic Artificial Intelligence." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 20 (2024): 22678. http://dx.doi.org/10.1609/aaai.v38i20.30294.

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The integration of learning and reasoning is one of the key challenges in artificial intelligence and machine learning today. The area of Neuro-Symbolic AI (NeSy) tackles this challenge by integrating symbolic reasoning with neural networks. In our recent work, we provided an introduction to NeSy by drawing several parallels to another field that has a rich tradition in integrating learning and reasoning, namely Statistical Relational Artificial Intelligence (StarAI).

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Shraddhaben R. Gajjar. "Neuro-symbolic ai for cloud intrusion detection: A hybrid intelligence approach." GSC Advanced Research and Reviews 22, no. 2 (2025): 142–44. https://doi.org/10.30574/gscarr.2025.22.2.0049.

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As cloud computing becomes more prevalent, ensuring robust cybersecurity is an ongoing challenge. Traditional intrusion detection systems (IDS) often struggle to keep pace with emerging cyber threats due to their reliance on static rule-based mechanisms. Neuro-Symbolic AI presents a hybrid intelligence approach that merges deep learning (neural networks) with symbolic reasoning, offering enhanced accuracy, explainability, and adaptability in intrusion detection. This paper investigates the role of Neuro-Symbolic AI in cloud security, detailing its application in threat detection, real-time res

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Letychevskyi, O. O., and Yu H. Tarasich. "Neuro-symbolic approach for the biological systems and processes research." PROBLEMS IN PROGRAMMING, no. 2-3 (September 2024): 271–79. https://doi.org/10.15407/pp2024.02-03.271.

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The current state and prospects for the application of neural network technology and systems built based on neural networks for studying biological systems and processes (in particular, the processes of virus-cell interaction) were considered in the article. In particular, the concept of the neuro-symbolic approach, which combines the use of neural networks and algebraic modelling, was described. The use of an algebraic approach in combination with neural networks allows us to obtain an effective formal proof of biological objects' properties (for example, cells' and viruses' properties) or pr

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Mekras, Nikolaos, Electra Mekra, and Christos Georgiou. "Neuro – symbolic AI for materials modelling and processes design." MATEC Web of Conferences 401 (2024): 14004. http://dx.doi.org/10.1051/matecconf/202440114004.

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This paper presents possibilities of combined use of Artificial Neural Networks (ANNs) with symbolic AI methods for improving the creation and the efficiency of models for materials and manufacturing processes. Besides a short presentation of one of the most basic and common ANNs architecture, which is the Multi-Layer Perceptron (MLP), and the presentation of main symbolic AI methods for knowledge representation and processing, the paper discusses their combined use and possibilities of hybrid neuro–symbolic AI applications for modelling materials and processes. This combined AI modelling appr

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Dissertations / Theses on the topic "Neuro-symbolic networks"

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Corchado, RodriÌguez Juan Manuel. "Neuro-symbolic model for real-time forecasting problems." Thesis, University of the West of Scotland, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.323760.

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Bader, Sebastian. "Neural-Symbolic Integration." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2009. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-25468.

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In this thesis, we discuss different techniques to bridge the gap between two different approaches to artiﬁcial intelligence: the symbolic and the connectionist paradigm. Both approaches have quite contrasting advantages and disadvantages. Research in the area of neural-symbolic integration aims at bridging the gap between them. Starting from a human readable logic program, we construct connectionist systems, which behave equivalently. Afterwards, those systems can be trained, and later the reﬁned knowledge be extracted.

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Osório, Fernando Santos. "Inss : un système hybride neuro-symbolique pour l'apprentissage automatique constructif." Grenoble INPG, 1998. https://tel.archives-ouvertes.fr/tel-00004899.

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Plusieurs méthodes ont été développées par l'Intelligence Artificielle pour reproduire certains aspects de l'intelligence humaine. Ces méthodes permettent de simuler les processus de raisonnement en s'appuyant sur les connaissances de base disponibles. Chaque méthode comporte des points forts, mais aussi des limitations. La réalisation de systèmes hybrides est une démarche courante Qui permet de combiner les points forts de chaque approche, et d'obtenir ainsi des performances plus élevées ou un champ d'application plus large. Un autre aspect très important du développement des systèmes hybride

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Hubert, Nicolas. "Mesure et enrichissement sémantiques des modèles à base d'embeddings pour la prédiction de liens dans les graphes de connaissances." Electronic Thesis or Diss., Université de Lorraine, 2024. http://www.theses.fr/2024LORR0059.

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Les modèles d'embeddings à base de graphes de connaissances ont considérablement gagné en popularité ces dernières années. Ces modèles apprennent une représentation vectorielle des entités et des relations des graphes de connaissances (GCs). Cette thèse explore spécifiquement le progrès de tels modèles pour la tâche de prédiction de lien (PL), qui est d'une importance capitale car elle se retrouve dans plusieurs applications telles que les systèmes de recommandation. Dans cette thèse, divers défis liés à l'utilisation des modèles d'embeddings de GCs pour la PL sont identifiés : la rareté des r

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Michulke, Daniel. "Evaluation Functions in General Game Playing." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-90566.

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While in traditional computer game playing agents were designed solely for the purpose of playing one single game, General Game Playing is concerned with agents capable of playing classes of games. Given the game's rules and a few minutes time, the agent is supposed to play any game of the class and eventually win it. Since the game is unknown beforehand, previously optimized data structures or human-provided features are not applicable. Instead, the agent must derive a strategy on its own. One approach to obtain such a strategy is to analyze the game rules and create a state evaluation funct

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Book chapters on the topic "Neuro-symbolic networks"

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Shakarian, Paulo, Chitta Baral, Gerardo I. Simari, Bowen Xi, and Lahari Pokala. "LNN: Logical Neural Networks." In Neuro Symbolic Reasoning and Learning. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-39179-8_6.

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Shakarian, Paulo, Chitta Baral, Gerardo I. Simari, Bowen Xi, and Lahari Pokala. "LTN: Logic Tensor Networks." In Neuro Symbolic Reasoning and Learning. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-39179-8_4.

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Shakarian, Paulo, Chitta Baral, Gerardo I. Simari, Bowen Xi, and Lahari Pokala. "Neuro Symbolic Reasoning with Ontological Networks." In Neuro Symbolic Reasoning and Learning. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-39179-8_5.

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Komendantskaya, Ekaterina, Krysia Broda, and Artur d’Avila Garcez. "Neuro-symbolic Representation of Logic Programs Defining Infinite Sets." In Artificial Neural Networks – ICANN 2010. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15819-3_39.

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Poli, R. "Discovery of Symbolic, Neuro-Symbolic and Neural Networks with Parallel Distributed Genetic Programming." In Artificial Neural Nets and Genetic Algorithms. Springer Vienna, 1998. http://dx.doi.org/10.1007/978-3-7091-6492-1_92.

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Dhanusha, C., A. V. Senthil Kumar, and V. S. Giridhar Akula. "Robust Cuckoo Search Enabled Fuzzy Neuro Symbolic Reasoning-Based Alzheimer’s Disease Prediction at Their Earlier Stages." In Computer Networks and Inventive Communication Technologies. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3035-5_65.

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Piano, Luca, Francesco Manigrasso, Alessandro Russo, and Lia Morra. "Enhancing Neuro-Symbolic Integration with Focal Loss: A Study on Logic Tensor Networks." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-71170-1_2.

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Pflueger, Maximilian, David J. Tena Cucala, and Egor V. Kostylev. "GNNQ: A Neuro-Symbolic Approach to Query Answering over Incomplete Knowledge Graphs." In The Semantic Web – ISWC 2022. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-19433-7_28.

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AbstractReal-world knowledge graphs (KGs) are usually incomplete—that is, miss some facts representing valid information. So, when applied to such KGs, standard symbolic query engines fail to produce answers that are expected but not logically entailed by the KGs. To overcome this issue, state-of-the-art ML-based approaches first embed KGs and queries into a low-dimensional vector space, and then produce query answers based on the proximity of the candidate entity and the query embeddings in the embedding space. This allows embedding-based approaches to obtain expected answers that are not log

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Yan, Rui, Gabriel Santos, Gethin Norman, David Parker, and Marta Kwiatkowska. "Partially Observable Stochastic Games with Neural Perception Mechanisms." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-71162-6_19.

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AbstractStochastic games are a well established model for multi-agent sequential decision making under uncertainty. In practical applications, though, agents often have only partial observability of their environment. Furthermore, agents increasingly perceive their environment using data-driven approaches such as neural networks trained on continuous data. We propose the model of neuro-symbolic partially-observable stochastic games (NS-POSGs), a variant of continuous-space concurrent stochastic games that explicitly incorporates neural perception mechanisms. We focus on a one-sided setting wit

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Hammer, Patrick. "Adaptive Neuro-Symbolic Network Agent." In Artificial General Intelligence. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-27005-6_8.

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Conference papers on the topic "Neuro-symbolic networks"

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Russo, Alessandro Sebastian, Lia Morra, Fabrizio Lamberti, and Paolo Emmanuel Ilario Dimasi. "ESRA: a Neuro-Symbolic Relation Transformer for Autonomous Driving." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10651426.

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Bizzarri, Alice, Brian Jalaian, Fabrizio Riguzzi, and Nathaniel D. Bastian. "A Neuro-Symbolic Artificial Intelligence Network Intrusion Detection System." In 2024 33rd International Conference on Computer Communications and Networks (ICCCN). IEEE, 2024. http://dx.doi.org/10.1109/icccn61486.2024.10637618.

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Pan, Yi, Yujia Zhang, Michael Kampffmeyer, and Xiaoguang Zhao. "PSAIR: A Neuro-Symbolic Approach to Zero-Shot Visual Grounding." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10649948.

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Xie, Xuan, Kristian Kersting, and Daniel Neider. "Neuro-Symbolic Verification of Deep Neural Networks." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/503.

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Formal verification has emerged as a powerful approach to ensure the safety and reliability of deep neural networks. However, current verification tools are limited to only a handful of properties that can be expressed as first-order constraints over the inputs and output of a network. While adversarial robustness and fairness fall under this category, many real-world properties (e.g., "an autonomous vehicle has to stop in front of a stop sign") remain outside the scope of existing verification technology. To mitigate this severe practical restriction, we introduce a novel framework for verify

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Aspis, Yaniv, Krysia Broda, Jorge Lobo, and Alessandra Russo. "Embed2Sym - Scalable Neuro-Symbolic Reasoning via Clustered Embeddings." In 19th International Conference on Principles of Knowledge Representation and Reasoning {KR-2022}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/kr.2022/44.

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Neuro-symbolic reasoning approaches proposed in recent years combine a neural perception component with a symbolic reasoning component to solve a downstream task. By doing so, these approaches can provide neural networks with symbolic reasoning capabilities, improve their interpretability and enable generalization beyond the training task. However, this often comes at the cost of poor training time, with potential scalability issues. In this paper, we propose a scalable neuro-symbolic approach, called Embed2Sym. We complement a two-stage (perception and reasoning) neural network architecture d

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Riveret, Regis, Son Tran, and Artur d'Avila Garcez. "Neuro-Symbolic Probabilistic Argumentation Machines." In 17th International Conference on Principles of Knowledge Representation and Reasoning {KR-2020}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/kr.2020/90.

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Neural-symbolic systems combine the strengths of neural networks and symbolic formalisms. In this paper, we introduce a neural-symbolic system which combines restricted Boltzmann machines and probabilistic semi-abstract argumentation. We propose to train networks on argument labellings explaining the data, so that any sampled data outcome is associated with an argument labelling. Argument labellings are integrated as constraints within restricted Boltzmann machines, so that the neural networks are used to learn probabilistic dependencies amongst argument labels. Given a dataset and an argument

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Dold, Dominik, Josep Soler Garrido, Victor Caceres Chian, Marcel Hildebrandt, and Thomas Runkler. "Neuro-symbolic computing with spiking neural networks." In ICONS: International Conference on Neuromorphic Systems. ACM, 2022. http://dx.doi.org/10.1145/3546790.3546824.

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Villanueva, J., V. Cruz, G. Reyes, and A. Benitez. "Extracting Refined Rules from Hybrid Neuro-Symbolic Systems." In 2006 International Joint Conference on Neural Networks. IEEE, 2006. http://dx.doi.org/10.1109/ijcnn.2006.247260.

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Cunnington, Daniel, Mark Law, Jorge Lobo, and Alessandra Russo. "Neuro-Symbolic Learning of Answer Set Programs from Raw Data." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/399.

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One of the ultimate goals of Artificial Intelligence is to assist humans in complex decision making. A promising direction for achieving this goal is Neuro-Symbolic AI, which aims to combine the interpretability of symbolic techniques with the ability of deep learning to learn from raw data. However, most current approaches require manually engineered symbolic knowledge, and where end-to-end training is considered, such approaches are either restricted to learning definite programs, or are restricted to training binary neural networks. In this paper, we introduce Neuro-Symbolic Inductive Learn

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Mghames, Sariah, Luca Castri, Marc Hanheide, and Nicola Bellotto. "A Neuro-Symbolic Approach for Enhanced Human Motion Prediction." In 2023 International Joint Conference on Neural Networks (IJCNN). IEEE, 2023. http://dx.doi.org/10.1109/ijcnn54540.2023.10191970.

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