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Artykuły w czasopismach na temat "Neuro-Symbolic Artificial intelligence"
Marra, Giuseppe. "From Statistical Relational to Neuro-Symbolic Artificial Intelligence". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 20 (24.03.2024): 22678. http://dx.doi.org/10.1609/aaai.v38i20.30294.
Pełny tekst źródłaMorel, Gilles. "Neuro-symbolic A.I. for the smart city". Journal of Physics: Conference Series 2042, nr 1 (1.11.2021): 012018. http://dx.doi.org/10.1088/1742-6596/2042/1/012018.
Pełny tekst źródłavan Bekkum, Michael, Maaike de Boer, Frank van Harmelen, André Meyer-Vitali i Annette ten Teije. "Modular design patterns for hybrid learning and reasoning systems". Applied Intelligence 51, nr 9 (18.06.2021): 6528–46. http://dx.doi.org/10.1007/s10489-021-02394-3.
Pełny tekst źródłaEbrahimi, Monireh, Aaron Eberhart, Federico Bianchi i Pascal Hitzler. "Towards bridging the neuro-symbolic gap: deep deductive reasoners". Applied Intelligence 51, nr 9 (6.02.2021): 6326–48. http://dx.doi.org/10.1007/s10489-020-02165-6.
Pełny tekst źródłaBarbosa, Raul, Douglas O. Cardoso, Diego Carvalho i Felipe M. G. França. "Weightless neuro-symbolic GPS trajectory classification". Neurocomputing 298 (lipiec 2018): 100–108. http://dx.doi.org/10.1016/j.neucom.2017.11.075.
Pełny tekst źródłaBahamid, Alala, Azhar Mohd Ibrahim i Amir Akramin Shafie. "Crowd evacuation with human-level intelligence via neuro-symbolic approach". Advanced Engineering Informatics 60 (kwiecień 2024): 102356. http://dx.doi.org/10.1016/j.aei.2024.102356.
Pełny tekst źródłaŠkrlj, Blaž, Matej Martinc, Nada Lavrač i Senja Pollak. "autoBOT: evolving neuro-symbolic representations for explainable low resource text classification". Machine Learning 110, nr 5 (14.04.2021): 989–1028. http://dx.doi.org/10.1007/s10994-021-05968-x.
Pełny tekst źródłaPrentzas, Jim, i Ioannis Hatzilygeroudis. "Neurules and connectionist expert systems: Unexplored neuro-symbolic reasoning aspects". Intelligent Decision Technologies 15, nr 4 (10.01.2022): 761–77. http://dx.doi.org/10.3233/idt-210211.
Pełny tekst źródłaShilov, Nikolay, Andrew Ponomarev i Alexander Smirnov. "The Analysis of Ontology-Based Neuro-Symbolic Intelligence Methods for Collaborative Decision Support". Informatics and Automation 22, nr 3 (22.05.2023): 576–615. http://dx.doi.org/10.15622/ia.22.3.4.
Pełny tekst źródłaKishor, Rabinandan. "Neuro-Symbolic AI: Bringing a new era of Machine Learning". International Journal of Research Publication and Reviews 03, nr 12 (2022): 2326–36. http://dx.doi.org/10.55248/gengpi.2022.31271.
Pełny tekst źródłaRozprawy doktorskie na temat "Neuro-Symbolic Artificial intelligence"
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.
Pełny tekst źródłaThe research conducted in this thesis is centered on the domain of safe urban driving, employing sensor fusion and reinforcement learning methodologies for the perception and control of autonomous vehicles (AV). The evolution and widespread integration of machine learning technologies have primarily propelled the proliferation of autonomous vehicles in recent years. However, substantial progress is requisite before achieving widespread adoption by the general populace. To accomplish its automation, autonomous vehicles necessitate the integration of an array of costly sensors, including cameras, radars, LiDARs, and ultrasonic sensors. In addition to their financial burden, these sensors exhibit susceptibility to environmental variables such as weather, a limitation not shared by human drivers who can navigate diverse conditions with a reliance on simple frontal vision. Moreover, the advent of decision-making neural network algorithms constitutes the core intelligence of autonomous vehicles. Deep Reinforcement Learning solutions, facilitating end-to-end driver policy learning, have found application in elementary driving scenarios, encompassing tasks like lane-keeping, steering control, and acceleration management. However, these algorithms demand substantial time and extensive datasets for effective training. In addition, safety must be considered throughout the development and deployment phases of autonomous vehicles.The first contribution of this thesis improves vehicle localization by fusing data from GPS and IMU sensors with an adaptation of a Kalman filter, ES-EKF, and a reduction of noise in IMU measurements.This method excels in urban environments marked by signal obstructions and elevated noise levels, effectively mitigating the adverse impact of noise in IMU sensor measurements, thereby maintaining localization accuracy and robustness. The algorithm is deployed and tested employing ground truth data on an embedded microcontroller. The second contribution introduces the DPPO-IL (Dynamic Proximal Policy Optimization with Imitation Learning) algorithm, designed to facilitate end-to-end automated parking while maintaining a steadfast focus on safety. This algorithm acquires proficiency in executing optimal parking maneuvers while navigating static and dynamic obstacles through exhaustive training incorporating simulated and real-world data.The third contribution is an end-to-end urban driving framework called GHRL. It incorporates vision and localization data and expert demonstrations expressed in the Answer Set Programming (ASP) rules to guide the hierarchical reinforcement learning (HRL) exploration policy and speed up the learning algorithm's convergence. When a critical situation occurs, the system relies on safety rules, which empower it to make prudent choices amidst unpredictable or hazardous conditions. GHRL is evaluated on the Carla NoCrash benchmark, and the results show that by incorporating logical rules, GHRL achieved better performance over state-of-the-art algorithms
Misino, Eleonora. "Deep Generative Models with Probabilistic Logic Priors". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24058/.
Pełny tekst źródłaOsório, Fernando Santos. "Inss : un système hybride neuro-symbolique pour l'apprentissage automatique constructif". Grenoble INPG, 1998. https://tel.archives-ouvertes.fr/tel-00004899.
Pełny tekst źródłaVarious Artificial Intelligence methods have been developed to reproduce intelligent human behaviour. These methods allow to reproduce some human reasoning process using the available knowledge. Each method has its advantages, but also some drawbacks. Hybrid systems combine different approaches in order to take advantage of their respective strengths. These hybrid intelligent systems also present the ability to acquire new knowledge from different sources and so to improve their application performance. This thesis presents our research in the field of hybrid neuro-symbolic systems, and in particular the study of machine learning tools used for constructive knowledge acquisition. We are interested in the automatic acquisition of theoretical knowledge (rules) and empirical knowledge (examples). We present a new hybrid system we implemented: INSS - Incremental Neuro-Symbolic System. This system allows knowledge transfer from the symbolic module to the connectionist module (Artificial Neural Network - ANN), through symbolic rule compilation into an ANN. We can refine the initial ANN knowledge through neural learning using a set of examples. The incremental ANN learning method used, the Cascade-Correlation algorithm, allows us to change or to add new knowledge to the network. Then, the system can also extract modified (or new) symbolic rules from the ANN and validate them. INSS is a hybrid machine learning system that implements a constructive knowledge acquisition method. We conclude by showing the results we obtained with this system in different application domains: ANN artificial problems(The Monk's Problems), computer aided medical diagnosis (Toxic Comas), a cognitive modelling task (The Balance Scale Problem) and autonomous robot control. The results we obtained show the improved performance of INSS and its advantages over others hybrid neuro-symbolic systems
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/.
Pełny tekst źródłaHubert, 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.
Pełny tekst źródłaKnowledge graph embedding models (KGEMs) have gained considerable traction in recent years. These models learn a vector representation of knowledge graph entities and relations, a.k.a. knowledge graph embeddings (KGEs). This thesis specifically explores the advancement of KGEMs for the link prediction (LP) task, which is of utmost importance as it underpins several downstream applications such as recommender systems. In this thesis, various challenges around the use of KGEMs for LP are identified: the scarcity of semantically rich resources, the unidimensional nature of evaluation frameworks, and the lack of semantic considerations in prevailing machine learning-based approaches. Central to this thesis is the proposition of novel solutions to these challenges. Firstly, the thesis contributes to the development of semantically rich resources: mainstream datasets for link prediction are enriched using schema-based information, EducOnto and EduKG are proposed to overcome the paucity of resources in the educational domain, and PyGraft is introduced as an innovative open-source tool for generating synthetic ontologies and knowledge graphs. Secondly, the thesis proposes a new semantic-oriented evaluation metric, Sem@K, offering a multi-dimensional perspective on model performance. Importantly, popular models are reassessed using Sem@K, which reveals essential insights into their respective capabilities and highlights the need for multi-faceted evaluation frameworks. Thirdly, the thesis delves into the development of neuro-symbolic approaches, transcending traditional machine learning paradigms. These approaches do not only demonstrate improved semantic awareness but also extend their utility to diverse applications such as recommender systems. In summary, the present work not only redefines the evaluation and functionality of knowledge graph embedding models but also sets the stage for more versatile, interpretable AI systems, underpinning future explorations at the intersection of machine learning and symbolic reasoning
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.
Pełny tekst źródłaKsiążki na temat "Neuro-Symbolic Artificial intelligence"
Xi, Bowen, i Lahari Pokala. Neuro Symbolic Reasoning and Learning. Springer, 2023.
Znajdź pełny tekst źródłaHitzler, Pascal, i Md Kamruzzaman Sarker, red. Neuro-Symbolic Artificial Intelligence: The State of the Art. IOS Press, 2021. http://dx.doi.org/10.3233/faia342.
Pełny tekst źródłaHitzler, P., i M. K. Sarker. Neuro-Symbolic Artificial Intelligence: The State of the Art. IOS Press, Incorporated, 2022.
Znajdź pełny tekst źródłaHitzler, P., i M. K. Sarker. Neuro-Symbolic Artificial Intelligence: The State of the Art. IOS Press, Incorporated, 2022.
Znajdź pełny tekst źródłaNeuro-Symbolic AI: Design Transparent and Trustworthy Systems That Understand the World As You Do. Packt Publishing, Limited, 2023.
Znajdź pełny tekst źródłaNeuro-Symbolic AI: Design Transparent and Trustworthy Systems That Understand the World As You Do. de Gruyter GmbH, Walter, 2023.
Znajdź pełny tekst źródłaCzęści książek na temat "Neuro-Symbolic Artificial intelligence"
Hammer, Patrick. "Adaptive Neuro-Symbolic Network Agent". W Artificial General Intelligence, 80–90. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-27005-6_8.
Pełny tekst źródłaShumsky, Sergey, i Oleg Baskov. "ADAM: A Prototype of Hierarchical Neuro-Symbolic AGI". W Artificial General Intelligence, 255–64. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-33469-6_26.
Pełny tekst źródłaShakarian, Paulo, Chitta Baral, Gerardo I. Simari, Bowen Xi i Lahari Pokala. "Fuzzy and Annotated Logic for Neuro Symbolic Artificial Intelligence". W Neuro Symbolic Reasoning and Learning, 15–31. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-39179-8_3.
Pełny tekst źródłaFdez-Riverola, Florentino, Juan M. Corchado i Jesús M. Torres. "Neuro-symbolic System for Forecasting Red Tides". W Artificial Intelligence and Cognitive Science, 45–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45750-x_6.
Pełny tekst źródłaKolonin, Anton. "Neuro-Symbolic Architecture for Experiential Learning in Discrete and Functional Environments". W Artificial General Intelligence, 106–15. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93758-4_12.
Pełny tekst źródłaLi, Lukai, Luping Shi i Rong Zhao. "A Vertical-Horizontal Integrated Neuro-Symbolic Framework Towards Artificial General Intelligence". W Artificial General Intelligence, 197–206. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-33469-6_20.
Pełny tekst źródłaYin, Chao, Quentin Cappart i Gilles Pesant. "An Improved Neuro-Symbolic Architecture to Fine-Tune Generative AI Systems". W Integration of Constraint Programming, Artificial Intelligence, and Operations Research, 279–88. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-60599-4_19.
Pełny tekst źródłaGolovko, Vladimir, Aliaksandr Kroshchanka, Mikhail Kovalev, Valery Taberko i Dzmitry Ivaniuk. "Neuro-Symbolic Artificial Intelligence: Application for Control the Quality of Product Labeling". W Communications in Computer and Information Science, 81–101. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60447-9_6.
Pełny tekst źródłaKestler, Hans A., Steffen Simon, Axel Baune, Friedhelm Schwenker i Günther Palm. "Object Classification Using Simple, Colour Based Visual Attention and a Hierarchical Neural Network for Neuro-symbolic Integration". W KI-99: Advances in Artificial Intelligence, 267–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48238-5_22.
Pełny tekst źródłaSmirnov, Alexander, Andrew Ponomarev i Nikolay Shilov. "Collaborative Decision Support with Ontology-Based Neuro-Symbolic Artificial Intelligence: Challenges and Conceptual Model". W Proceedings of the Sixth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’22), 51–59. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-19620-1_6.
Pełny tekst źródłaStreszczenia konferencji na temat "Neuro-Symbolic Artificial intelligence"
Bizzarri, Alice, Brian Jalaian, Fabrizio Riguzzi i Nathaniel D. Bastian. "A Neuro-Symbolic Artificial Intelligence Network Intrusion Detection System". W 2024 33rd International Conference on Computer Communications and Networks (ICCCN), 1–9. IEEE, 2024. http://dx.doi.org/10.1109/icccn61486.2024.10637618.
Pełny tekst źródłaJohnstone, David, Larbi Esmahi i Ali Dewan. "A Neuro-Symbolic Learning System for Analyzing Listing Images in the Short-Term Rental Industry". W 2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT), 205–11. IEEE, 2024. http://dx.doi.org/10.1109/iaict62357.2024.10617670.
Pełny tekst źródłaRaedt, Luc de, Sebastijan Dumančić, Robin Manhaeve i Giuseppe Marra. "From Statistical Relational to Neuro-Symbolic Artificial Intelligence". W Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/688.
Pełny tekst źródłaDemir, Caglar, i Axel-Cyrille Ngonga Ngomo. "Neuro-Symbolic Class Expression Learning". W Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/403.
Pełny tekst źródłaAlers-Valentín, Hilton, Sandiway Fong i J. Vega-Riveros. "Modeling Syntactic Knowledge With Neuro-Symbolic Computation". W 15th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2023. http://dx.doi.org/10.5220/0011718500003393.
Pełny tekst źródłaCunnington, Daniel, Mark Law, Jorge Lobo i Alessandra Russo. "Neuro-Symbolic Learning of Answer Set Programs from Raw Data". W Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/399.
Pełny tekst źródłaXie, Xuan, Kristian Kersting i Daniel Neider. "Neuro-Symbolic Verification of Deep Neural Networks". W Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/503.
Pełny tekst źródłaThomas, Christo Kurisummoottil, i Walid Saad. "Neuro-Symbolic Artificial Intelligence (AI) for Intent based Semantic Communication". W GLOBECOM 2022 - 2022 IEEE Global Communications Conference. IEEE, 2022. http://dx.doi.org/10.1109/globecom48099.2022.10001097.
Pełny tekst źródłaKouvaros, Panagiotis. "Towards Formal Verification of Neuro-symbolic Multi-agent Systems". W Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/800.
Pełny tekst źródłaHatzilygeroudis, I., i J. Prentzas. "Controlling the Production of Neuro-symbolic Rules". W 2012 IEEE 24th International Conference on Tools with Artificial Intelligence (ICTAI 2012). IEEE, 2012. http://dx.doi.org/10.1109/ictai.2012.148.
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