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Artigos de revistas 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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1

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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Abhishek Sharma. "Commonsense reasoning in AI systems." International Journal of Science and Research Archive 14, no. 3 (2025): 1638–57. https://doi.org/10.30574/ijsra.2025.14.3.0865.

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Within artificial intelligence, mostly in natural language processing and machine learning, there programmatic systems fabricated for reproducing commonsense reasoning, which remains a challenge for AI developers. Worded simply, such systems fail in reasoning modulations which are implicit, contextual, and have other inferences intermixed which humans solve subconsciously or unconsciously. The objective of this research is to determine how commonsense reasoning is relevant to AI and suggest certain methodologies for its operationalization based on knowledge systems, deep-learning, and hybrid n

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Škrlj, Blaž, Matej Martinc, Nada Lavrač, and Senja Pollak. "autoBOT: evolving neuro-symbolic representations for explainable low resource text classification." Machine Learning 110, no. 5 (2021): 989–1028. http://dx.doi.org/10.1007/s10994-021-05968-x.

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AbstractLearning from texts has been widely adopted throughout industry and science. While state-of-the-art neural language models have shown very promising results for text classification, they are expensive to (pre-)train, require large amounts of data and tuning of hundreds of millions or more parameters. This paper explores how automatically evolved text representations can serve as a basis for explainable, low-resource branch of models with competitive performance that are subject to automated hyperparameter tuning. We present autoBOT (automatic Bags-Of-Tokens), an autoML approach suitabl

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Verheyen, Lara, Jérôme Botoko Ekila, Jens Nevens, Paul Van Eecke, and Katrien Beuls. "Neuro-symbolic procedural semantics for explainable visual dialogue." PLOS One 20, no. 5 (2025): e0323098. https://doi.org/10.1371/journal.pone.0323098.

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This paper introduces a novel approach to visual dialogue that is based on neuro-symbolic procedural semantics. The approach builds further on earlier work on procedural semantics for visual question answering and expands it with neuro-symbolic mechanisms that handle the challenges that are inherent to dialogue, in particular the incremental nature of the information that is conveyed. Concretely, we introduce (i) the use of a conversation memory as a data structure that explicitly and incrementally represents the information that is expressed during the subsequent turns of a dialogue, and (ii)

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Prentzas, Jim, and Ioannis Hatzilygeroudis. "Neurules and connectionist expert systems: Unexplored neuro-symbolic reasoning aspects." Intelligent Decision Technologies 15, no. 4 (2022): 761–77. http://dx.doi.org/10.3233/idt-210211.

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Neuro-symbolic approaches combine neural and symbolic methods. This paper explores aspects regarding the reasoning mechanisms of two neuro-symbolic approaches, that is, neurules and connectionist expert systems. Both provide reasoning and explanation facilities. Neurules are a type of neuro-symbolic rules tightly integrating the neural and symbolic components, giving pre-eminence to the symbolic component. Connectionist expert systems give pre-eminence to the connectionist component. This paper explores reasoning aspects about neurules and connectionist expert systems that have not been previo

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Shilov, Nikolay, Andrew Ponomarev, and Alexander Smirnov. "The Analysis of Ontology-Based Neuro-Symbolic Intelligence Methods for Collaborative Decision Support." Informatics and Automation 22, no. 3 (2023): 576–615. http://dx.doi.org/10.15622/ia.22.3.4.

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The neural network approach to AI, which has become especially widespread in the last decade, has two significant limitations – training of a neural network, as a rule, requires a very large number of samples (not always available), and the resulting models often are not well interpretable, which can reduce their credibility. The use of symbols as the basis of collaborative processes, on the one hand, and the proliferation of neural network AI, on the other hand, necessitate the synthesis of neural network and symbolic paradigms in relation to the creation of collaborative decision support sys

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Kishor, Rabinandan. "Neuro-Symbolic AI: Bringing a new era of Machine Learning." International Journal of Research Publication and Reviews 03, no. 12 (2022): 2326–36. http://dx.doi.org/10.55248/gengpi.2022.31271.

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Processing Natural Language using machines is not a new concept. Back in 1940 researchers estimated the importance of a machine that could translate one language to another. Further, during 1957-1970 researchers split into two divisions concerning NLP: symbolic and stochastic. This paper presents an extensive review of recent breakthroughs in Neuro Symbolic Artificial Intelligence (NSAI), an area of AI research which seeks to combine traditional rules-based AI approaches with modern deep learning techniques. Neuro Symbolic models have already demonstrated the capability to outperform state-of-

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Showemimo, Paul. "Neuro-Symbolic Architectures for Explainable Multi-Modal Plagiarism Detection in Academic Assessment." International Journal of Advances in Engineering and Management 7, no. 7 (2025): 483–99. https://doi.org/10.35629/5252-0707483499.

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The pervasive integration of Artificial Intelligence (AI) into educational ecosystems has introduced both unprecedented opportunities and complex challenges, particularly concerning academic integrity. While AI-driven tools have become indispensable for identifying traditional forms of plagiarism, the emergence of sophisticated content generation models and multi-modal assignments necessitates a paradigm shift in detection methodologies. This paper proposes a novel MultiModal Neuro-Symbolic Assessment System (MNSAS) architecture specifically designed for explainable plagiarism detection. Our a

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Smirnov, A. V., A. V. Ponomarev, N. G. Shilov, and T. V. Levashova. "Collaborative Decision Support Systems Based on Neuro-Symbolic Artificial Intelligence: Problems and Generalized Conceptual Model." Scientific and Technical Information Processing 50, no. 6 (2023): 635–45. http://dx.doi.org/10.3103/s0147688223060151.

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Boya Marqas, Ridwan, Saman M. Almufti, and Rezhna Azad Yusif. "Unveiling explainability in artificial intelligence: a step to-‎wards transparent AI." International Journal of Scientific World 11, no. 1 (2025): 13–20. https://doi.org/10.14419/f2agrs86.

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Explainability in artificial intelligence (AI) is an essential factor for building transparent, trustworthy, and ethical systems, particularly in ‎high-stakes domains such as healthcare, finance, justice, and autonomous systems. This study examines the foundations of AI explainability, ‎its critical role in fostering trust, and the current methodologies used to interpret AI models, such as post-hoc techniques, intrinsically inter-‎pretable models, and hybrid approaches. Despite these advancements, challenges persist, including trade-offs between accuracy and inter-‎pretability, scalability, et

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20

Skryagin, Arseny, Daniel Ochs, Devendra Singh Dhami, and Kristian Kersting. "Scalable Neural-Probabilistic Answer Set Programming." Journal of Artificial Intelligence Research 78 (November 16, 2023): 579–617. http://dx.doi.org/10.1613/jair.1.15027.

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The goal of combining the robustness of neural networks and the expressiveness of symbolic methods has rekindled the interest in Neuro-Symbolic AI. Deep Probabilistic Programming Languages (DPPLs) have been developed for probabilistic logic programming to be carried out via the probability estimations of deep neural networks (DNNs). However, recent SOTA DPPL approaches allow only for limited conditional probabilistic queries and do not offer the power of true joint probability estimation. In our work, we propose an easy integration of tractable probabilistic inference within a DPPL. To this en

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Adeshina, Yusuff Taofeek. "A Neuro-Symbolic Artificial Intelligence and Zero-Knowledge Blockchain Framework for a Patient-Owned Digital-Twin Marketplace in U.S. Value-Based Care." International Journal of Research Publication and Reviews 6, no. 6 (2025): 5804–21. https://doi.org/10.55248/gengpi.6.0625.21105.

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Souici-Meslati, Labiba, and Mokhtar Sellami. "A Hybrid Neuro-Symbolic Approach for Arabic Handwritten Word Recognition." Journal of Advanced Computational Intelligence and Intelligent Informatics 10, no. 1 (2006): 17–25. http://dx.doi.org/10.20965/jaciii.2006.p0017.

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In this article, we suggest a system that automatically constructs knowledge based artificial neural networks (KBANN) for the holistic recognition of handwritten Arabic words in limited lexicons. To build a neuro-symbolic KBANN classifier for a given vocabulary, ideal samples of its words are first submitted to a structural feature extraction module. The analysis of the presence and possible occurrence numbers for these features in the considered lexicon enables to generate a symbolic knowledge base reflecting a hierarchical classification of the words. A rules-to-network translation algorithm

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23

Akhter, Shamima, Joynul Arefin, Md Shahadat Hossen, et al. "Neuro-Symbolic AI for IoT-Driven Smart Cities: A Next-Generation Framework for Urban Intelligence." Journal of Computer Science and Technology Studies 7, no. 2 (2025): 36–55. https://doi.org/10.32996/jcsts.2025.7.2.4.

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The integration of the Internet of Things (IoT) and Artificial Intelligence (AI) is revolutionizing urban landscapes by enhancing operational efficiency, resource management, and sustainability in smart cities. IoT enables real-time data acquisition through distributed sensor networks, while AI processes this data to facilitate intelligent decision-making across critical urban domains, including transportation, energy management, environmental monitoring, public safety, and healthcare. Despite its potential, this convergence presents critical challenges such as data heterogeneity, security vul

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Onchis, Darian, Codruta Istin, and Eduard Hogea. "A Neuro-Symbolic Classifier with Optimized Satisfiability for Monitoring Security Alerts in Network Traffic." Applied Sciences 12, no. 22 (2022): 11502. http://dx.doi.org/10.3390/app122211502.

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We introduce in this paper a neuro-symbolic predictive model based on Logic Tensor Networks, capable of discriminating and at the same time of explaining the bad connections, called alerts or attacks, and the normal connections. The proposed classifier incorporates both the ability of deep neural networks to improve on their own through learning from experience and the interpretability of the results provided by the symbolic artificial intelligence approach. Compared to other existing solutions, we advance in the discovery of potential security breaches from a cognitive perspective. By introdu

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Papadimitriou, Stergios, and Constantinos Terzidis. "Symbolic adaptive neuro-fuzzy inference for data mining of heterogenous data." Intelligent Data Analysis 7, no. 4 (2003): 327–46. http://dx.doi.org/10.3233/ida-2003-7405.

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Feng, Yufei, Xiaoyu Yang, Xiaodan Zhu, and Michael Greenspan. "Neuro-symbolic Natural Logic with Introspective Revision for Natural Language Inference." Transactions of the Association for Computational Linguistics 10 (2022): 240–56. http://dx.doi.org/10.1162/tacl_a_00458.

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Abstract We introduce a neuro-symbolic natural logic framework based on reinforcement learning with introspective revision. The model samples and rewards specific reasoning paths through policy gradient, in which the introspective revision algorithm modifies intermediate symbolic reasoning steps to discover reward-earning operations as well as leverages external knowledge to alleviate spurious reasoning and training inefficiency. The framework is supported by properly designed local relation models to avoid input entangling, which helps ensure the interpretability of the proof paths. The propo

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Yuan, Ye, Bo Tang, Tianfei Zhou, Zhiwei Zhang, and Jianbin Qin. "nsDB: Architecting the Next Generation Database by Integrating Neural and Symbolic Systems." Proceedings of the VLDB Endowment 17, no. 11 (2024): 3283–89. http://dx.doi.org/10.14778/3681954.3682000.

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In this paper, we propose nsDB, a novel neuro-symbolic database system that integrates neural and symbolic system architectures natively to address the weaknesses of each, providing a strong database capable of data managing, model learning, and complex analytical query processing over multi-modal data. We employ a real-world NBA data analytical query as an example to illustrate the functionality of each component in nsDB and highlight the research challenges to build it. We then present the key design principles and our preliminary attempts to address them. In a nutshell, we envision that the

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Pallagani, Vishal, Bharath Chandra Muppasani, Kaushik Roy, et al. "On the Prospects of Incorporating Large Language Models (LLMs) in Automated Planning and Scheduling (APS)." Proceedings of the International Conference on Automated Planning and Scheduling 34 (May 30, 2024): 432–44. http://dx.doi.org/10.1609/icaps.v34i1.31503.

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Automated Planning and Scheduling is among the growing areas in Artificial Intelligence (AI) where mention of LLMs has gained popularity. Based on a comprehensive review of 126 papers, this paper investigates eight categories based on the unique applications of LLMs in addressing various aspects of planning problems: language translation, plan generation, model construction, multi-agent planning, interactive planning, heuristics optimization, tool integration, and brain-inspired planning. For each category, we articulate the issues considered and existing gaps. A critical insight resulting fro

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Palconit, Maria Gemel B., Ronnie S. Concepcion II, Jonnel D. Alejandrino, et al. "Three-Dimensional Stereo Vision Tracking of Multiple Free-Swimming Fish for Low Frame Rate Video." Journal of Advanced Computational Intelligence and Intelligent Informatics 25, no. 5 (2021): 639–46. http://dx.doi.org/10.20965/jaciii.2021.p0639.

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Three-dimensional multiple fish tracking has gained significant research interest in quantifying fish behavior. However, most tracking techniques use a high frame rate, which is currently not viable for real-time tracking applications. This study discusses multiple fish-tracking techniques using low-frame-rate sampling of stereo video clips. The fish were tagged and tracked based on the absolute error of the predicted indices using past and present fish centroid locations and a deterministic frame index. In the predictor sub-system, linear regression and machine learning algorithms intended fo

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Rosemarie, Velik. "BRAIN Journal - Quo vadis, Intelligent Machine?" BRAIN - Broad Research in Artificial Intelligence and Neuroscience 1, no. 4 (2010): 13–22. https://doi.org/10.5281/zenodo.1037355.

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ABSTRACT Artificial Intelligence (AI) is a branch of computer science concerned with making computers behave like humans. At least this was the original idea. However, it turned out that this was no easy task to solve. This article aims to give a comprehensible review on the last 60 years of artificial intelligence taking a philosophical viewpoint. It is outlined what happened so far in AI, what is currently going on in this research area, and what can be expected in the future. The goal is to mediate an understanding for the developments and changes in thinking in the course of time about how

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Pandit, Ms Yukta. "A Survey on Advancing Medical Diagnostics and Healthcare with Generative AI: Techniques, Applications, and Future Prospects." International Journal for Research in Applied Science and Engineering Technology 13, no. 6 (2025): 3537–43. https://doi.org/10.22214/ijraset.2025.72838.

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Generative artificial intelligence (AI) is reshaping modern healthcare by augmenting diagnostic accuracy, accelerating imaging workflows, and enabling data-driven clinical decisions. This paper surveys the recent evolution and clinical integration of generative models—such as GANs, diffusion architectures, and transformer-based systems—in medical diagnostics, with a focus on radiology. Across 219 peer-reviewed studies and 47 clinical implementations, these models demonstrate improvements in image clarity, artifact reduction, and rare disease detection, while reducing documentation burden and e

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Hua, Hua, Dongxu Li, Ruiqi Li, Peng Zhang, Jochen Renz, and Anthony Cohn. "Towards Explainable Action Recognition by Salient Qualitative Spatial Object Relation Chains." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 5 (2022): 5710–18. http://dx.doi.org/10.1609/aaai.v36i5.20513.

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In order to be trusted by humans, Artificial Intelligence agents should be able to describe rationales behind their decisions. One such application is human action recognition in critical or sensitive scenarios, where trustworthy and explainable action recognizers are expected. For example, reliable pedestrian action recognition is essential for self-driving cars and explanations for real-time decision making are critical for investigations if an accident happens. In this regard, learning-based approaches, despite their popularity and accuracy, are disadvantageous due to their limited interpre

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Prentzas, Jim, and Ioannis Hatzilygeroudis. "Assessment of life insurance applications: an approach integrating neuro-symbolic rule-based with case-based reasoning." Expert Systems 33, no. 2 (2015): 145–60. http://dx.doi.org/10.1111/exsy.12137.

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Biryukov, D. N., and A. S. Dudkin. "Explainability and interpretability are important aspects in ensuring the security of decisions made by intelligent systems (review article)." Scientific and Technical Journal of Information Technologies, Mechanics and Optics 25, no. 3 (2025): 373–86. https://doi.org/10.17586/2226-1494-2025-25-3-373-386.

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The issues of trust in decisions made (formed) by intelligent systems are becoming more and more relevant. A systematic review of Explicable Artificial Intelligence (XAI) methods and tools aimed at bridging the gap between the complexity of neural networks and the need for interpretability of results for end users is presented. A theoretical analysis of the differences between explainability and interpretability in the context of artificial intelligence as well as their role in ensuring the security of decisions made by intelligent systems is carried out. It is shown that explainability implie

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Ekechi, Chijioke C. "Explainable AI Models for Autonomous UAV Decision Making in Complex Terrains: A Comparative Analysis." International Journal of Future Engineering Innovations 2, no. 4 (2025): 29–36. https://doi.org/10.54660/ijfei.2025.2.4.29-36.

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This paper investigated the integration of Explainable Artificial Intelligence (XAI) models into Unmanned Aerial Vehicle (UAV) systems to enhance decision making in complex and dynamic terrains. The motivation stemmed from the growing reliance on autonomous UAVs in mission critical operations where transparency, trust, and accountability are essential. The study presented a structured overview of various XAI techniques ranging from inherently interpretable models such as decision trees and rule based systems to post hoc methods like LIME, SHAP, and Grad CAM, as well as hybrid approaches includ

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Vijayabaskar, Santhosh. "Harnessing Generative AI for Risk Management and Fraud Detection in Fintech: A New Era of Human-Machine Collaboration." International Journal of Scientific Research and Management (IJSRM) 8, no. 04 (2020): 369–79. http://dx.doi.org/10.18535/ijsrm/v8i04.ec01.

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Hybrid Intelligence Systems (HIS) represent a paradigm shift in problem-solving methodologies by integrating human expertise with Artificial Intelligence (AI) and Robotic Process Automation (RPA). This paper explores the mechanisms, applications, benefits, challenges, and future directions of HIS in the context of complex problem-solving. Through collaborative synergies between human cognition and machine intelligence, HIS enhances decision-making accuracy, efficiency, and innovation. Human experts contribute domain knowledge, contextual understanding, and ethical reasoning, while AI algorithm

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Hu, Yiwen, and Markus J. Buehler. "Deep language models for interpretative and predictive materials science." APL Machine Learning 1, no. 1 (2023): 010901. http://dx.doi.org/10.1063/5.0134317.

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Machine learning (ML) has emerged as an indispensable methodology to describe, discover, and predict complex physical phenomena that efficiently help us learn underlying functional rules, especially in cases when conventional modeling approaches cannot be applied. While conventional feedforward neural networks are typically limited to performing tasks related to static patterns in data, recursive models can both work iteratively based on a changing input and discover complex dynamical relationships in the data. Deep language models can model flexible modalities of data and are capable of learn

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R, Gayathri, Gobinath T, Muthumari A, and R. S. V. Rama Swathi. "ENHANCED AI BASED FEATURE EXTRACTION TECHNIQUE IN MULTIMEDIA IMAGE RETRIEVAL." ICTACT Journal on Image and Video Processing 13, no. 4 (2023): 3021–27. http://dx.doi.org/10.21917/ijivp.2023.0429.

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In the era of rapid technological advancements, the demand for efficient and accurate identification and retrieval of information from multimedia images has seen a substantial increase. To meet this growing demand, artificial intelligence (AI)-based technologies, particularly feature extraction techniques, have gained significant popularity. Feature extraction involves the extraction of salient features from multimedia images, such as edges, lines, curves, textures, and colors, with the aim of representing the data in a more suitable format for analysis. This paper presents an enhanced AI-base

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Sarker, Md Kamruzzaman, Lu Zhou, Aaron Eberhart, and Pascal Hitzler. "Neuro-symbolic artificial intelligence." AI Communications, September 16, 2021, 1–13. http://dx.doi.org/10.3233/aic-210084.

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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.

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Hitzler, Pascal, Aaron Eberhart, Monireh Ebrahimi, Md Kamruzzaman Sarker, and Lu Zhou. "Neuro-Symbolic Approaches in Artificial Intelligence." National Science Review, March 4, 2022. http://dx.doi.org/10.1093/nsr/nwac035.

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Abstract Neuro-Symbolic Artificial Intelligence refers to a field of research and applications that combines machine learning methods based on artificial neural networks, such as deep learning, with symbolic approaches to computing and Artificial Intelligence (AI), as can be found for example in the AI subfield of Knowledge Representation and Reasoning. Neuro-Symbolic AI has a long history, however it remained a rather niche topic until recently, when landmark advances in machine learning – prompted by deep learning – caused a significant rise in interest and research activity in combining neu

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Bhuyan, Bikram Pratim, Amar Ramdane-Cherif, Ravi Tomar, and T. P. Singh. "Neuro-symbolic artificial intelligence: a survey." Neural Computing and Applications, June 6, 2024. http://dx.doi.org/10.1007/s00521-024-09960-z.

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Lu, Zhen, Imran Afridi, Hong Jin Kang, Ivan Ruchkin, and Xi Zheng. "Surveying neuro-symbolic approaches for reliable artificial intelligence of things." Journal of Reliable Intelligent Environments, July 26, 2024. http://dx.doi.org/10.1007/s40860-024-00231-1.

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AbstractThe integration of Artificial Intelligence (AI) with the Internet of Things (IoT), known as the Artificial Intelligence of Things (AIoT), enhances the devices’ processing and analysis capabilities and disrupts such sectors as healthcare, industry, and oil. However, AIoT’s complexity and scale are challenging for traditional machine learning (ML). Deep learning offers a solution but has limited testability, verifiability, and interpretability. In turn, the neuro-symbolic paradigm addresses these challenges by combining the robustness of symbolic AI with the flexibility of DL, enabling A

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Miguel-Angel, Mendez Lucero, and Belle Vaishak. "Boolean Connectives and Deep Learning: Three Interpretations." July 13, 2023. https://doi.org/10.5281/zenodo.8145155.

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Bueff, Andreas, and Vaishak Belle. "Learning explanatory logical rules in non-linear domains: a neuro-symbolic approach." Machine Learning, April 8, 2024. http://dx.doi.org/10.1007/s10994-024-06538-7.

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AbstractDeep neural networks, despite their capabilities, are constrained by the need for large-scale training data, and often fall short in generalisation and interpretability. Inductive logic programming (ILP) presents an intriguing solution with its data-efficient learning of first-order logic rules. However, ILP grapples with challenges, notably the handling of non-linearity in continuous domains. With the ascent of neuro-symbolic ILP, there’s a drive to mitigate these challenges, synergising deep learning with relational ILP models to enhance interpretability and create logical decision b

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Shi, Tuo, Hui Zhang, Shiyu Cui, et al. "Stochastic neuro-fuzzy system implemented in memristor crossbar arrays." Science Advances 10, no. 12 (2024). http://dx.doi.org/10.1126/sciadv.adl3135.

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Neuro-symbolic artificial intelligence has garnered considerable attention amid increasing industry demands for high-performance neural networks that are interpretable and adaptable to previously unknown problem domains with minimal reconfiguration. However, implementing neuro-symbolic hardware is challenging due to the complexity in symbolic knowledge representation and calculation. We experimentally demonstrated a memristor-based neuro-fuzzy hardware based on TiN/TaO x /HfO x /TiN chips that is superior to its silicon-based counterpart in terms of throughput and energy efficiency by using ar

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Roy, Anand. "Building Human-Like Artificial Intelligence: A Research Synthesis." April 28, 2025. https://doi.org/10.5281/zenodo.15299516.

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The creation of Artificial General Intelligence (AGI), particularly systems exhibiting human-like cognitive capabilities, represents a long-standing and formidable scientific challenge. This paper synthesizes research across artificial intelligence, cognitive science, neuroscience, and philosophy to provide a comprehensive overview of the multifaceted endeavor to build human-like AI. We examine the cognitive foundations differentiating human and artificial intelligence, delving into fundamental challenges such as embodiment and symbol grounding , causality and commonsense reasoning , human-lik

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Amlashi, Danial M., Alexander Voelz, and Dimitris Karagiannis. "Artificial Intelligence and Internet of Things: A Neuro-Symbolic Approach for Automated Platform Configuration." Neurosymbolic Artificial Intelligence 1 (June 2025). https://doi.org/10.1177/29498732251340187.

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Complex Internet of Things (IoT) environments present significant challenges in device discovery and platform configuration, especially as the number of interconnected devices continues to grow. This research addresses these challenges by leveraging the complementary strengths of symbolic and connectionist artificial intelligence (AI) within a neuro-symbolic system. We propose a comprehensive approach to IoT platform configuration that integrates neuro-symbolic reasoning and conceptual modelling techniques, enhancing both efficiency and explainability. Following the process model of design sci

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Huang, Zhen. "Introducing Neuro-Symbolic Artificial Intelligence to Humanities and Social Sciences: Why Is It Possible and What Can Be Done?" TEM Journal, November 25, 2022, 1863–70. http://dx.doi.org/10.18421/tem114-54.

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With the support of artificial intelligence (AI), the smart applications in all walks of life have brought great changes to human society. Not only being concerned, analysed and criticized by scholars from Humanities and social sciences, AI also plays an important role in empirical research methods, thus facilitating the transformation of research paradigms in these fields. At present, neuro-symbolic AI, as a new product of the integration of two major factions in the field of artificial intelligence - connectionism and symbolism, has high application value in studying and solving the humanist

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Shivadekar, Samit. "Artificial Intelligence for Cognitive Systems Deep Learning, Neuro-symbolic Integration, and Human-centric Intelligence." SSRN Electronic Journal, 2025. https://doi.org/10.2139/ssrn.5344224.

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He, Hao-Yuan, Wang-Zhou Dai, and Ming Li. "Reduced implication-bias logic loss for neuro-symbolic learning." Machine Learning, January 30, 2024. http://dx.doi.org/10.1007/s10994-023-06436-4.

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