Relevant bibliographies by topics / Neuro-Symbolic Artificial intelligence

Academic literature on the topic 'Neuro-Symbolic Artificial intelligence'

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Published: 7 September 2024

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Journal articles on the topic "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 (March 24, 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 (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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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 (June 18, 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 large number of hybrid systems by composing only a small set of elementary patterns as building blocks. The main contributions of this paper are: 1) a taxonomically organised vocabulary to describe both processes and data structures used in hybrid systems; 2) a set of 15+ design patterns for hybrid AI systems organized in a set of elementary patterns and a set of compositional patterns; 3) an application of these design patterns in two realistic use-cases for hybrid AI systems. Our patterns reveal similarities between systems that were not recognized until now. Finally, our design patterns extend and refine Kautz’s earlier attempt at categorizing neuro-symbolic architectures.
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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 (February 6, 2021): 6326–48. http://dx.doi.org/10.1007/s10489-020-02165-6.

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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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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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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 (April 14, 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 suitable for low resource learning scenarios, where both the hardware and the amount of data required for training are limited. The proposed approach consists of an evolutionary algorithm that jointly optimizes various sparse representations of a given text (including word, subword, POS tag, keyword-based, knowledge graph-based and relational features) and two types of document embeddings (non-sparse representations). The key idea of autoBOT is that, instead of evolving at the learner level, evolution is conducted at the representation level. The proposed method offers competitive classification performance on fourteen real-world classification tasks when compared against a competitive autoML approach that evolves ensemble models, as well as state-of-the-art neural language models such as BERT and RoBERTa. Moreover, the approach is explainable, as the importance of the parts of the input space is part of the final solution yielded by the proposed optimization procedure, offering potential for meta-transfer learning.
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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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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 (May 22, 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 systems. The article presents the results of an analytical review in the field of ontology-oriented neuro-symbolic artificial intelligence with an emphasis on solving problems of knowledge exchange during collaborative decision support. Specifically, the review attempts to answer two questions: 1. how symbolic knowledge, represented as an ontology, can be used to improve AI agents operating on the basis of neural networks (knowledge transfer from a person to AI agents); 2. how symbolic knowledge, represented as an ontology, can be used to interpret decisions made by AI agents and explain these decisions (transfer of knowledge from an AI agent to a person). As a result of the review, recommendations were formulated on the choice of methods for introducing symbolic knowledge into neural network models, and promising areas of ontology-oriented methods for explaining neural networks were identified.
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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-the-art deep learning models in domains such as image and video reasoning. Such models not only performed better when trained on a fraction of dataset compared with traditional machine learning models, but also solved an underlined issue called generalization of deep neural network systems. We also find that symbolic models are good in visual question answering (VQA). In this paper, we also review research results related to Neuro Symbolic AI with the objective of exploring the importance of such AI systems and how it would shape the future of AI as a whole. We discuss different types of dataset of Visual Question Answering (VQA) tasks based on NSAI and extensive comparison of performance of different NSAI models. Later, the article focuses on the contemporary real time application of NSAI systems and how NSAI is shaping the world’s different sectors including finance, healthcare, and cyber security.
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Dissertations / Theses on the topic "Neuro-Symbolic Artificial intelligence"

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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'une série de capteurs coûteux (e.g. caméras, radars, LiDAR et capteurs à ultrasons). En plus de leur fardeau financier, ces capteurs présentent une sensibilité aux variations telles que la météo, une limitation non partagée par les conducteurs humains qui peuvent naviguer dans des conditions diverses en se fiant à une vision frontale simple. Par ailleurs, l'avènement des algorithmes neuronaux de prise de décision constitue l'intelligence fondamentale des véhicules autonomes. Les solutions d'apprentissage profond par renforcement, facilitant l'apprentissage de la politique du conducteur de bout en bout, ont trouvé application dans des scénarios de conduite élémentaires, englobant des tâches telles que le maintien dans la voie, le contrôle de la direction et la gestion de l'accélération. Cependant, il s'avère que ces algorithmes sont coûteux en temps d'exécution et nécessitent de large ensembles de données pour un entraînement efficace. De plus, la sécurité doit être prise en compte tout au long des phases de développement et de déploiement des véhicules autonomes.La première contribution de cette thèse améliore la localisation des véhicules en fusionnant les mesures des capteurs GPS et IMU avec une adaptation d'un filtre de Kalman, ES-EKF, et une réduction du bruit des mesures IMU. L'algorithme est déployé et testé en utilisant des données de vérité terrain sur un microcontrôleur. La deuxième contribution propose l'algorithme DPPO-IL (Dynamic Proximal Policy Optimization with Imitation Learning), conçu pour faciliter le stationnement automatisé en accordant une attention toute particulière à la sécurité. Cet algorithme apprend à exécuter des manœuvres de stationnement optimales tout en naviguant entre des d'obstacles statiques et dynamiques grâce à un entraînement complet intégrant des données simulées et réelles. La troisième contribution est un framework de conduite urbaine de bout en bout appelé guided hierarchical reinforcement Learning (GHRL). Il intègre des données de vision et de localisation ainsi que des démonstrations d'experts exprimées avec des règles ASP (Answer Set Programming) pour guider la politique d'exploration de l'apprentissage par renforcement hiérarchique et accélérer la convergence de l'algorithme. Lorsqu'une situation critique se produit, le système s'appuie également sur des règles liées à la sécurité pour faire des choix judicieux dans des conditions imprévisibles ou dangereuses. GHRL est évalué sur le jeu de données NoCrash du simulateur Carla et les résultats montrent qu'en incorporant des règles logiques, GHRL obtient de meilleures performances que les algorithmes de l'état de l'art
The 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
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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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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 ressources sémantiquement riches, la nature unidimensionnelle des cadres d'évaluation, et le manque de considérations sémantiques dans les approches d'apprentissage automatique. Cette thèse propose des solutions novatrices à ces défis. Premièrement, elle contribue au développement de ressources sémantiquement riches : les jeux de données principaux pour la prédiction de lien sont enrichis en utilisant des informations basées sur des schémas, EducOnto et EduKG sont proposés pour surmonter la pénurie de ressources dans le domaine éducatif, et PyGraft est introduit comme un outil innovant pour générer des ontologies synthétiques et des graphes de connaissances. Deuxièmement, la thèse propose une nouvelle métrique d'évaluation orientée sémantique, Sem@K, offrant une perspective multidimensionnelle sur la performance des modèles. Il est important de souligner que les modèles populaires sont réévalués en utilisant Sem@K, ce qui révèle des aspects essentiels et jusqu'alors inexplorés de leurs capacités respectives et souligne le besoin de cadres d'évaluation multidimensionnels. Troisièmement, la thèse se penche sur le développement d'approches neuro-symboliques, transcendant les paradigmes traditionnels de l'apprentissage automatique. Ces approches ne démontrent pas seulement une meilleure capacité sémantique dans leurs prédictions, mais étendent également leur utilité à diverses applications telles que les systèmes de recommandation. En résumé, le présent travail ne redéfinit pas seulement l'évaluation et la fonctionnalité des modèles d'embeddings de GCs, mais prépare également le terrain pour des systèmes d'intelligence artificielle plus polyvalents et interprétables, soutenant les explorations futures à l'intersection de l'apprentissage automatique et du raisonnement symbolique
Knowledge 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
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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 Artificial intelligence"

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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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Book chapters on the topic "Neuro-Symbolic Artificial intelligence"

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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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Shumsky, Sergey, and Oleg Baskov. "ADAM: A Prototype of Hierarchical Neuro-Symbolic AGI." In Artificial General Intelligence, 255–64. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-33469-6_26.

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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, 15–31. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-39179-8_3.

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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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Kolonin, Anton. "Neuro-Symbolic Architecture for Experiential Learning in Discrete and Functional Environments." In Artificial General Intelligence, 106–15. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93758-4_12.

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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, 197–206. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-33469-6_20.

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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, 279–88. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-60599-4_19.

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Golovko, Vladimir, Aliaksandr Kroshchanka, Mikhail Kovalev, Valery Taberko, and Dzmitry Ivaniuk. "Neuro-Symbolic Artificial Intelligence: Application for Control the Quality of Product Labeling." In 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.

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Kestler, Hans A., Steffen Simon, Axel Baune, Friedhelm Schwenker, and Günther Palm. "Object Classification Using Simple, Colour Based Visual Attention and a Hierarchical Neural Network for Neuro-symbolic Integration." In 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.

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Smirnov, Alexander, Andrew Ponomarev, and Nikolay Shilov. "Collaborative Decision Support with Ontology-Based Neuro-Symbolic Artificial Intelligence: Challenges and Conceptual Model." In 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.

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Conference papers on the topic "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), 1–9. IEEE, 2024. http://dx.doi.org/10.1109/icccn61486.2024.10637618.

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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), 205–11. 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}. 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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Demir, Caglar, and Axel-Cyrille Ngonga Ngomo. "Neuro-Symbolic Class Expression Learning." In 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.

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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 novel neuro-symbolic class expression learning model, DRILL, to mitigate this limitation. By learning non-myopic heuristic functions with deep Q-learning, DRILL efficiently steers the standard search procedure in a quasi-ordered search space towards goal states. Our extensive experiments on 4 benchmark datasets and 390 learning problems suggest that DRILL converges to goal states at least 2.7 times faster than state-of-the-art models on all learning problems. The results of our statistical significance test confirms that DRILL converges to goal states significantly faster (p-value <1%) than state-of-the-art models on all benchmark datasets. We provide an open-source implementation of DRILL, including pre-trained models, training and evaluation scripts.
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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}. California: 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 Learner (NSIL), an approach that trains a general neural network to extract latent concepts from raw data, whilst learning symbolic knowledge that maps latent concepts to target labels. The novelty of our approach is a method for biasing the learning of symbolic knowledge, based on the in-training performance of both neural and symbolic components. We evaluate NSIL on three problem domains of different complexity, including an NP-complete problem. Our results demonstrate that NSIL learns expressive knowledge, solves computationally complex problems, and achieves state-of-the-art performance in terms of accuracy and data efficiency. Code and technical appendix: https://github.com/DanCunnington/NSIL
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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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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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Kouvaros, Panagiotis. "Towards Formal Verification of Neuro-symbolic Multi-agent Systems." In 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.

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This paper outlines some of the key methods we developed towards the formal verification of multi- agent systems, covering both symbolic and connectionist systems. It discusses logic-based methods for the verification of unbounded multi-agent systems (i.e., systems composed of an arbitrary number of homogeneous agents, e.g., robot swarms), optimisation approaches for establishing the robustness of neural network models, and methods for analysing properties of neuro-symbolic multi-agent systems.
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Hatzilygeroudis, I., and J. Prentzas. "Controlling the Production of Neuro-symbolic Rules." In 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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