Bibliografias temáticas / Neuro-Symbolic Artificial intelligence

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Literatura científica selecionada sobre o tema "Neuro-Symbolic Artificial intelligence"

Autor: Grafiati
Publicado: 7 de setembro de 2024
Última modificação: 31 de julho de 2025

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Artigos de revistas sobre o assunto "Neuro-Symbolic Artificial intelligence"

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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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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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van Bekkum, Michael, Maaike de Boer, Frank van Harmelen, André Meyer-Vitali, and Annette ten Teije. "Modular design patterns for hybrid learning and reasoning systems." Applied Intelligence 51, no. 9 (2021): 6528–46. http://dx.doi.org/10.1007/s10489-021-02394-3.

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AbstractThe unification of statistical (data-driven) and symbolic (knowledge-driven) methods is widely recognized as one of the key challenges of modern AI. Recent years have seen a large number of publications on such hybrid neuro-symbolic AI systems. That rapidly growing literature is highly diverse, mostly empirical, and is lacking a unifying view of the large variety of these hybrid systems. In this paper, we analyze a large body of recent literature and we propose a set of modular design patterns for such hybrid, neuro-symbolic systems. We are able to describe the architecture of a very l
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Ebrahimi, Monireh, Aaron Eberhart, Federico Bianchi, and Pascal Hitzler. "Towards bridging the neuro-symbolic gap: deep deductive reasoners." Applied Intelligence 51, no. 9 (2021): 6326–48. http://dx.doi.org/10.1007/s10489-020-02165-6.

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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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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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Barbosa, Raul, Douglas O. Cardoso, Diego Carvalho, and Felipe M. G. França. "Weightless neuro-symbolic GPS trajectory classification." Neurocomputing 298 (July 2018): 100–108. http://dx.doi.org/10.1016/j.neucom.2017.11.075.

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Awolesi Abolanle Ogunboyo. "Neuro-Symbolic Generative AI for Explainable Reasoning." International Journal of Science and Research Archive 16, no. 1 (2025): 121–25. https://doi.org/10.30574/ijsra.2025.16.1.2019.

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The integration of neural and symbolic systems termed neuro-symbolic AI presents a compelling path toward explainable reasoning in Artificial Intelligence (AI). While deep learning models excel at pattern recognition and generative capabilities, their opaque decision-making process has raised concerns about transparency, interpretability, and trustworthiness. This research investigates the convergence of generative AI and neuro-symbolic architectures to enhance explainable reasoning. Employing a mixed-methods methodology grounded in empirical evaluation, knowledge representation, and symbolic
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Kamali, Danial, Elham J. Barezi, and Parisa Kordjamshidi. "NeSyCoCo: A Neuro-Symbolic Concept Composer for Compositional Generalization." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 4 (2025): 4184–93. https://doi.org/10.1609/aaai.v39i4.32439.

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Compositional generalization is crucial for artificial intelligence agents to solve complex vision-language reasoning tasks. Neuro-symbolic approaches have demonstrated promise in capturing compositional structures, but they face critical challenges: (a) reliance on predefined predicates for symbolic representations that limit adaptability, (b) difficulty in extracting predicates from raw data, and (c) using non-differentiable operations for combining primitive concepts. To address these issues, we propose NeSyCoCo, a neuro-symbolic framework that leverages large language models (LLMs) to gene
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Bahamid, Alala, Azhar Mohd Ibrahim, and Amir Akramin Shafie. "Crowd evacuation with human-level intelligence via neuro-symbolic approach." Advanced Engineering Informatics 60 (April 2024): 102356. http://dx.doi.org/10.1016/j.aei.2024.102356.

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Teses / dissertações sobre o assunto "Neuro-Symbolic Artificial intelligence"

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Bhuyan, Bikram Pratim. "Neuro-symbolic knowledge hypergraphs : knowledge representation and learning in neuro-symbolic artificial intelligence." Electronic Thesis or Diss., université Paris-Saclay, 2025. http://www.theses.fr/2025UPASG024.

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L'intégration du raisonnement symbolique et de l'apprentissage neuronal en Intelligence Artificielle (IA) est devenue de plus en plus cruciale à mesure que la demande de modèles capables de gérer des données complexes, dynamiques et interconnectées croît. Alors que les approches traditionnelles ont fait des progrès dans ces domaines séparément, un cadre unifié combinant ces paradigmes est essentiel pour faire progresser la capacité de l'IA à interpréter, apprendre et prédire dans des environnements réels.Malgré les avancées des modèles symboliques et neuronaux, la littérature existante révèle
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Albilani, Mohamad. "Neuro-symbolic deep reinforcement learning for safe urban driving using low-cost sensors." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAS008.

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La recherche effectuée dans cette thèse concerne le domaine de la conduite urbaine sûre, en utilisant des méthodes de fusion de capteurs et d'apprentissage par renforcement pour la perception et le contrôle des véhicules autonomes (VA). L'évolution généralisée des technologies d'apprentissage automatique ont principalement propulsé la prolifération des véhicules autonomes ces dernières années. Cependant, des progrès substantiels sont nécessaires avant d'atteindre une adoption généralisée par le grand public. Pour accomplir son automatisation, les véhicules autonomes nécessitent l'intégration d
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Misino, Eleonora. "Deep Generative Models with Probabilistic Logic Priors." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24058/.

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Many different extensions of the VAE framework have been introduced in the past. How­ ever, the vast majority of them focused on pure sub­-symbolic approaches that are not sufficient for solving generative tasks that require a form of reasoning. In this thesis, we propose the probabilistic logic VAE (PLVAE), a neuro-­symbolic deep generative model that combines the representational power of VAEs with the reasoning ability of probabilistic ­logic programming. The strength of PLVAE resides in its probabilistic ­logic prior, which provides an interpretable structure to the latent space that can b
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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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Giuliani, Luca. "Extending the Moving Targets Method for Injecting Constraints in Machine Learning." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23885/.

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Informed Machine Learning is an umbrella term that comprises a set of methodologies in which domain knowledge is injected into a data-driven system in order to improve its level of accuracy, satisfy some external constraint, and in general serve the purposes of explainability and reliability. The said topid has been widely explored in the literature by means of many different techniques. Moving Targets is one such a technique particularly focused on constraint satisfaction: it is based on decomposition and bi-level optimization and proceeds by iteratively refining the target labels through a m
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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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Livros sobre o assunto "Neuro-Symbolic Artificial intelligence"

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Bhuyan, Bikram Pratim, Amar Ramdane-Cherif, Thipendra P. Singh, and Ravi Tomar. Neuro-Symbolic Artificial Intelligence. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-8171-3.

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Xi, Bowen, and Lahari Pokala. Neuro Symbolic Reasoning and Learning. Springer, 2023.

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Hitzler, Pascal, and Md Kamruzzaman Sarker, eds. Neuro-Symbolic Artificial Intelligence: The State of the Art. IOS Press, 2021. http://dx.doi.org/10.3233/faia342.

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Hitzler, P., and M. K. Sarker. Neuro-Symbolic Artificial Intelligence: The State of the Art. IOS Press, Incorporated, 2022.

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Hitzler, P., and M. K. Sarker. Neuro-Symbolic Artificial Intelligence: The State of the Art. IOS Press, Incorporated, 2022.

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Neuro-Symbolic AI: Design Transparent and Trustworthy Systems That Understand the World As You Do. Packt Publishing, Limited, 2023.

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Neuro-Symbolic AI: Design Transparent and Trustworthy Systems That Understand the World As You Do. de Gruyter GmbH, Walter, 2023.

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Capítulos de livros sobre o assunto "Neuro-Symbolic Artificial intelligence"

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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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Shumsky, Sergey, and Oleg Baskov. "ADAM: A Prototype of Hierarchical Neuro-Symbolic AGI." In Artificial General Intelligence. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-33469-6_26.

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Bhuyan, Bikram Pratim, Amar Ramdane-Cherif, Thipendra P. Singh, and Ravi Tomar. "The Emergence of Neuro-Symbolic Artificial Intelligence." In Studies in Computational Intelligence. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-97-8171-3_1.

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Shakarian, Paulo, Chitta Baral, Gerardo I. Simari, Bowen Xi, and Lahari Pokala. "Fuzzy and Annotated Logic for Neuro Symbolic Artificial Intelligence." In Neuro Symbolic Reasoning and Learning. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-39179-8_3.

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Carnevali, Laura, and Marco Lippi. "Neuro-Symbolic Artificial Intelligence for Safety Engineering." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-68738-9_35.

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Fenske, Ole, Sebastian Bader, and Thomas Kirste. "Neuro-symbolic Artificial Intelligence for Patient Monitoring." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-74640-6_2.

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Fdez-Riverola, Florentino, Juan M. Corchado, and Jesús M. Torres. "Neuro-symbolic System for Forecasting Red Tides." In Artificial Intelligence and Cognitive Science. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45750-x_6.

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Li, Lukai, Luping Shi, and Rong Zhao. "A Vertical-Horizontal Integrated Neuro-Symbolic Framework Towards Artificial General Intelligence." In Artificial General Intelligence. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-33469-6_20.

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Kolonin, Anton. "Neuro-Symbolic Architecture for Experiential Learning in Discrete and Functional Environments." In Artificial General Intelligence. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93758-4_12.

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Yin, Chao, Quentin Cappart, and Gilles Pesant. "An Improved Neuro-Symbolic Architecture to Fine-Tune Generative AI Systems." In Integration of Constraint Programming, Artificial Intelligence, and Operations Research. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-60599-4_19.

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Trabalhos de conferências sobre o assunto "Neuro-Symbolic Artificial intelligence"

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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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Alers-Valentín, Hilton, José Maldonado-Torres, and J. Vega-Riveros. "Limitations of Tokenizers for Building a Neuro-Symbolic Lexicon." In 17th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013386100003890.

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Zandigohar, Mehrshad, and Gunar Schirner. "Grasp-Sym: A Neuro-Symbolic Approach to Robust Grasp Classification." In 2025 6th International Conference on Artificial Intelligence, Robotics and Control (AIRC). IEEE, 2025. https://doi.org/10.1109/airc64931.2025.11077518.

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Johnstone, David, Larbi Esmahi, and Ali Dewan. "A Neuro-Symbolic Learning System for Analyzing Listing Images in the Short-Term Rental Industry." In 2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT). IEEE, 2024. http://dx.doi.org/10.1109/iaict62357.2024.10617670.

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Raedt, Luc de, Sebastijan Dumančić, Robin Manhaeve, and Giuseppe Marra. "From Statistical Relational to Neuro-Symbolic Artificial Intelligence." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/688.

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Neuro-symbolic and statistical relational artificial intelligence both integrate frameworks for learning with logical reasoning. This survey identifies several parallels across seven different dimensions between these two fields. These cannot only be used to characterize and position neuro-symbolic artificial intelligence approaches but also to identify a number of directions for further research.
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Demir, Caglar, and Axel-Cyrille Ngonga Ngomo. "Neuro-Symbolic Class Expression Learning." 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.2023/403.

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Models computed using deep learning have been effectively applied to tackle various problems in many disciplines. Yet, the predictions of these models are often at most post-hoc and locally explainable. In contrast, class expressions in description logics are ante-hoc and globally explainable. Although state-of-the-art symbolic machine learning approaches are being successfully applied to learn class expressions, their application at large scale has been hindered by their impractical runtimes. Arguably, the reliance on myopic heuristic functions contributes to this limitation. We propose a nov
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Alers-Valentín, Hilton, Sandiway Fong, and J. Vega-Riveros. "Modeling Syntactic Knowledge With Neuro-Symbolic Computation." In 15th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2023. http://dx.doi.org/10.5220/0011718500003393.

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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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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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Thomas, Christo Kurisummoottil, and Walid Saad. "Neuro-Symbolic Artificial Intelligence (AI) for Intent based Semantic Communication." In GLOBECOM 2022 - 2022 IEEE Global Communications Conference. IEEE, 2022. http://dx.doi.org/10.1109/globecom48099.2022.10001097.

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