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Relevant bibliographies by topics / Neuro-symbolic

Academic literature on the topic 'Neuro-symbolic'

Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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Journal articles on the topic "Neuro-symbolic"

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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 (April 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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Arabshahi, Forough, Jennifer Lee, Mikayla Gawarecki, Kathryn Mazaitis, Amos Azaria, and Tom Mitchell. "Conversational Neuro-Symbolic Commonsense Reasoning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 6 (May 18, 2021): 4902–11. http://dx.doi.org/10.1609/aaai.v35i6.16623.

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In order for conversational AI systems to hold more natural and broad-ranging conversations, they will require much more commonsense, including the ability to identify unstated presumptions of their conversational partners. For example, in the command "If it snows at night then wake me up early because I don't want to be late for work" the speaker relies on commonsense reasoning of the listener to infer the implicit presumption that they wish to be woken only if it snows enough to cause traffic slowdowns. We consider here the problem of understanding such imprecisely stated natural language commands given in the form of if-(state), then-(action), because-(goal) statements. More precisely, we consider the problem of identifying the unstated presumptions of the speaker that allow the requested action to achieve the desired goal from the given state (perhaps elaborated by making the implicit presumptions explicit). We release a benchmark data set for this task, collected from humans and annotated with commonsense presumptions. We present a neuro-symbolic theorem prover that extracts multi-hop reasoning chains, and apply it to this problem. Furthermore, to accommodate the reality that current AI commonsense systems lack full coverage, we also present an interactive conversational framework built on our neuro-symbolic system, that conversationally evokes commonsense knowledge from humans to complete its reasoning chains.

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Sathasivam, Saratha. "Acceleration technique for neuro symbolic integration." Applied Mathematical Sciences 9 (2015): 409–17. http://dx.doi.org/10.12988/ams.2015.48670.

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Rosemarie Velik. "The Neuro-Symbolic Code of Perception." Journal of Cognitive Science 11, no. 2 (December 2010): 161–80. http://dx.doi.org/10.17791/jcs.2010.11.2.161.

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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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Prentzas, Jim, and Ioannis Hatzilygeroudis. "Neurules and connectionist expert systems: Unexplored neuro-symbolic reasoning aspects." Intelligent Decision Technologies 15, no. 4 (January 10, 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 previously addressed. As far as neurules are concerned, an aspect playing a role in conflict resolution (i.e., order of neurules) is explored. Experimental results show an improvement in reasoning efficiency. As far as connectionist expert systems are concerned, variations of the reasoning mechanism are explored. Experimental results are presented for them as well showing that one of the variations generally performs better than the others.

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Morel, Gilles. "Neuro-symbolic A.I. for the smart city." Journal of Physics: Conference Series 2042, no. 1 (November 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 the smart city, and point the way towards a complete integration of the two technologies, compatible with standard software.

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Siyaev, Aziz, and Geun-Sik Jo. "Neuro-Symbolic Speech Understanding in Aircraft Maintenance Metaverse." IEEE Access 9 (2021): 154484–99. http://dx.doi.org/10.1109/access.2021.3128616.

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Sathasivam, Saratha. "Applying Different Learning Rules in Neuro-Symbolic Integration." Advanced Materials Research 433-440 (January 2012): 716–20. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.716.

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Pseudo inverse learning rule and new activation unction performance will be evaluated and compared with the primitive learning rule, Hebb rule. Comparisons are made between these three rules to see which rule is better or outperformed other rules in the aspects of computation time, memory and complexity. From the computer simulation that has been carried out, the new activation function performs better than the other two learning methods.

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Nauck, D. D. "Neuro-fuzzy learning with symbolic and numeric data." Soft Computing - A Fusion of Foundations, Methodologies and Applications -1, no. 1 (July 14, 2003): 1. http://dx.doi.org/10.1007/s00500-003-0294-7.

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Dissertations / Theses on the topic "Neuro-symbolic"

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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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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 be easily changed in order to apply the model to different scenarios. We provide empirical results of our approach by training PLVAE on a base task and then using the same model to generalize to novel tasks that involve reasoning with the same set of symbols.

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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 hybrides intelligents est leur capacité d'acquérir de nouvelles connaissances à partir de plusieurs sources différentes et de les faire évoluer. Dans cette thèse, nous avons développé des recherches sur les systèmes hybrides neuro-symboliques, et en particulier sur l'acquisition incrémentale de connaissances à partir de connaissances théoriques (règles) et empiriques (exemples). Un nouveau système hybride, nommé système INSS - Incremental Neuro-Symbolic System, a été étudié et réalisé. Ce système permet le transfert de connaissances déclaratives (règles symboliques) d'un module symbolique vers un module connexionniste (réseau de neurones artificiel - RNA) à travers un convertisseur de règles en réseau. Les connaissances du réseau ainsi obtenu sont affinées par un processus d'apprentissage à partir d'exemples. Ce raffinement se fait soit par ajout de nouvelles connaissances, soit par correction des incohérences, grâce à l'utilisation d'un réseau constructif de type Cascade-Correlation. Une méthode d'extraction incrémentale de règles a été intégrée au système INSS, ainsi que des algorithmes de validation des connaissances qui ont permis de mieux coupler les modules connexionniste et symbolique. Le système d'apprentissage automatique INSS a été conçu pour l'acquisition constructive (incrémentale) de connaissances. Le système a été testé sur plusieurs applications, en utilisant des problèmes académiques et des problèmes réels (diagnostic médical, modélisation cognitive et contrôle d'un robot autonome). Les résultats montrent que le système INSS a des performances supérieures et de nombreux avantages par rapport aux autres systèmes hybrides du même type
Various 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

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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 master step which is in charge of enforcing the constraints, while the training phase is delegated to a learner. In this work, we extend the algorithm in order to deal with semi-supervised learning and soft constraints. In particular, we focus our empirical evaluation on both regression and classification tasks involving monotonicity shape constraints. We demonstrate that our method is robust with respect to its hyperparameters, as well as being able to generalize very well while reducing the number of violations on the enforced constraints. Additionally, the method can even outperform, both in terms of accuracy and constraint satisfaction, other state-of-the-art techniques such as Lattice Models and Semantic-based Regularization with a Lagrangian Dual approach for automatic hyperparameter tuning.

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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 function that can be subsequently used to direct the agent to promising states in the match. In this thesis we will discuss existing methods and present a general approach on how to construct such an evaluation function. Each topic is discussed in a modular fashion and evaluated along the lines of quality and efficiency, resulting in a strong agent.

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Books on the topic "Neuro-symbolic"

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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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Book chapters on the topic "Neuro-symbolic"

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Hammer, Patrick. "Adaptive Neuro-Symbolic Network Agent." In Artificial General Intelligence, 80–90. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-27005-6_8.

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Burattini, Ernesto, Massimo De Gregorio, Victor M. G. Ferreira, and Felipe M. G. França. "NSP: a Neuro–Symbolic Processor." In Artificial Neural Nets Problem Solving Methods, 9–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44869-1_2.

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Kocoń, Jan, Joanna Baran, Marcin Gruza, Arkadiusz Janz, Michał Kajstura, Przemysław Kazienko, Wojciech Korczyński, Piotr Miłkowski, Maciej Piasecki, and Joanna Szołomicka. "Neuro-Symbolic Models for Sentiment Analysis." In Computational Science – ICCS 2022, 667–81. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08754-7_69.

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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, 45–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45750-x_6.

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Corchado, Juan M., M. Lourdes Borrajo, María A. Pellicer, and J. Carlos Yáñez. "Neuro-symbolic System for Business Internal Control." In Advances in Data Mining, 1–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30185-1_1.

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Samsonovich, Alexei V. "One Possibility of a Neuro-Symbolic Integration." In Studies in Computational Intelligence, 428–37. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96993-6_47.

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Azizan, Farah Liyana, Saratha Sathasivam, Majid Khan Majahar Ali, and Shehab Abdulhabib Saeed Alzaeemi. "Solving HornSAT Fuzzy Logic Neuro-symbolic Integration." In Studies in Systems, Decision and Control, 49–64. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04028-3_5.

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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, 419–23. Vienna: Springer Vienna, 1998. http://dx.doi.org/10.1007/978-3-7091-6492-1_92.

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Prentzas, Jim, and Ioannis Hatzilygeroudis. "Neurules-A Type of Neuro-symbolic Rules: An Overview." In Combinations of Intelligent Methods and Applications, 145–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19618-8_9.

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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, 301–4. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15819-3_39.

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Conference papers on the topic "Neuro-symbolic"

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Sathasivam, Saratha. "Neuro-symbolic Performance Comparison." In 2010 Second International Conference on Computer Engineering and Applications. IEEE, 2010. http://dx.doi.org/10.1109/iccea.2010.8.

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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}. California: 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 designed to solve a downstream task end-to-end with a symbolic optimisation method for extracting learned latent concepts. Specifically, the trained perception network generates clusters in embedding space that are identified and labelled using symbolic knowledge and a symbolic solver. With the latent concepts identified, a neuro-symbolic model is constructed by combining the perception network with the symbolic knowledge of the downstream task, resulting in a model that is interpretable and transferable. Our evaluation shows that Embed2Sym outperforms state-of-the-art neuro-symbolic systems on benchmark tasks in terms of training time by several orders of magnitude while providing similar if not better accuracy.

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Sathasivam, Saratha. "Abduction in Neuro Symbolic Integration." In 2011 Sixth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA). IEEE, 2011. http://dx.doi.org/10.1109/bic-ta.2011.50.

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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}. California: 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 argumentation graph as prior knowledge, for every example/case K in the dataset, we use a so-called K-maxconsistent labelling of the graph, and an explanation of case K refers to a K-maxconsistent labelling of the given argumentation graph. The abilities of the proposed system to predict correct labellings were evaluated and compared with standard machine learning techniques. Experiments revealed that such argumentation Boltzmann machines can outperform other classification models, especially in noisy settings.

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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}. California: 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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Shiqi, Shen, Shweta Shinde, Soundarya Ramesh, Abhik Roychoudhury, and Prateek Saxena. "Neuro-Symbolic Execution: Augmenting Symbolic Execution with Neural Constraints." In Network and Distributed System Security Symposium. Reston, VA: Internet Society, 2019. http://dx.doi.org/10.14722/ndss.2019.23530.

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Sathasivam, Saratha, and Muraly Velavan. "NEURO-SYMBOLIC INTEGRATION USING PSEUDO INVERSE RULE." In Annual International Conference on Advances in Distributed and Parallel Computing ADPC 2010. Global Science and Technology Forum, 2010. http://dx.doi.org/10.5176/978-981-08-7656-2atai2010-20.

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Kasihmuddin, Mohd Shareduwan Mohd, Saratha Sathasivam, and Mohd Asyraf Mansor. "Artificial bee colony in neuro - Symbolic integration." In PROCEEDINGS OF THE 24TH NATIONAL SYMPOSIUM ON MATHEMATICAL SCIENCES: Mathematical Sciences Exploration for the Universal Preservation. Author(s), 2017. http://dx.doi.org/10.1063/1.4995912.

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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}. California: 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 verifying neural networks, named neuro-symbolic verification. The key idea is to use neural networks as part of the otherwise logical specification, enabling the verification of a wide variety of complex, real-world properties, including the one above. A defining feature of our framework is that it can be implemented on top of existing verification infrastructure for neural networks, making it easily accessible to researchers and practitioners.

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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. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3546790.3546824.

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