Дисертації з теми "Neural-symbolic"
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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.
Повний текст джерелаTownsend, Joseph Paul. "Artificial development of neural-symbolic networks." Thesis, University of Exeter, 2014. http://hdl.handle.net/10871/15162.
Повний текст джерелаXiao, Chunyang. "Neural-Symbolic Learning for Semantic Parsing." Thesis, Université de Lorraine, 2017. http://www.theses.fr/2017LORR0268/document.
Повний текст джерелаOur goal in this thesis is to build a system that answers a natural language question (NL) by representing its semantics as a logical form (LF) and then computing the answer by executing the LF over a knowledge base. The core part of such a system is the semantic parser that maps questions to logical forms. Our focus is how to build high-performance semantic parsers by learning from (NL, LF) pairs. We propose to combine recurrent neural networks (RNNs) with symbolic prior knowledge expressed through context-free grammars (CFGs) and automata. By integrating CFGs over LFs into the RNN training and inference processes, we guarantee that the generated logical forms are well-formed; by integrating, through weighted automata, prior knowledge over the presence of certain entities in the LF, we further enhance the performance of our models. Experimentally, we show that our approach achieves better performance than previous semantic parsers not using neural networks as well as RNNs not informed by such prior knowledge
Mann, Jordyn(Jordyn L. ). "Neural Bayesian goal inference for symbolic planning domains." Thesis, Massachusetts Institute of Technology, 2021. https://hdl.handle.net/1721.1/130701.
Повний текст джерелаCataloged from the official PDF of thesis.
Includes bibliographical references (pages 51-52).
There are several reasons for which one may aim to infer the short- and long-term goals of agents in diverse physical domains. As increasingly powerful autonomous systems come into development, it is conceivable that they may eventually need to accurately infer the goals of humans. There are also more immediate reasons for which this sort of inference may be desirable, such as in the use case of intelligent personal assistants. This thesis introduces a neural Bayesian approach to goal inference in multiple symbolic planning domains and compares the results of this approach to the results of a recently developed Monte Carlo Bayesian inference method known as Sequential Inverse Plan Search (SIPS). SIPS is based on sequential Monte Carlo inference for Bayesian inversion of probabilistic plan search in Planning Domain Definition Language (PDDL) domains. In addition to the neural architectures, the thesis also introduces approaches for converting PDDL predicate state representations to numerical arrays and vectors suitable for input to the neural networks. The experimental results presented indicate that for the domains investigated, in cases where the training set is representative of the test set, the neural approach provides similar accuracy results to SIPS in the later portions of the observation sequences with a far shorter amortized time cost. However, in earlier timesteps of those observation sequences and in cases where the training set is less similar to the testing set, SIPS outperforms the neural approach in terms of accuracy. These results indicate that a model-based inference method where SIPS uses a neural proposal based on the neural networks designed in this thesis could have the potential to combine the advantages of both goal inference approaches by improving the speed of SIPS inference while maintaining generalizability and high accuracy throughout the timesteps of the observation sequences.
by Jordyn Mann.
M. Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
Noda, Itsuki. "Neural Networks that Learn Symbolic and Structred Representation of Information." Kyoto University, 1995. http://hdl.handle.net/2433/154663.
Повний текст джерелаKyoto University (京都大学)
0048
新制・課程博士
博士(工学)
甲第5860号
工博第1404号
新制||工||978(附属図書館)
UT51-95-B205
京都大学大学院工学研究科電気工学専攻
(主査)教授 長尾 真, 教授 池田 克夫, 教授 矢島 脩三
学位規則第4条第1項該当
Chichlowski, Kazimierz O. "Modelling and recognition of continuous and symbolic data using artificial neural networks." Thesis, University of East Anglia, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.320829.
Повний текст джерелаChen, Hsinchun, P. Buntin, Linlin She, S. Sutjahjo, C. Sommer, and D. Neely. "Expert Prediction, Symbolic Learning, and Neural Networks: An Experiment on Greyhound Racing." IEEE, 1994. http://hdl.handle.net/10150/105472.
Повний текст джерелаFor our research, we investigated a different problem-solving scenario called game playing, which is unstructured, complex, and seldom-studied. We considered several real-life game-playing scenarios and decided on greyhound racing. The large amount of historical information involved in the search poses a challenge for both human experts and machine-learning algorithms. The questions then become: Can machine-learning techniques reduce the uncertainty in a complex game-playing scenario? Can these methods outperform human experts in prediction? Our research sought to answer these questions.
Chen, Hsinchun. "Machine Learning for Information Retrieval: Neural Networks, Symbolic Learning, and Genetic Algorithms." Wiley Periodicals, Inc, 1995. http://hdl.handle.net/10150/106427.
Повний текст джерелаInformation retrieval using probabilistic techniques has attracted significant attention on the part of researchers in information and computer science over the past few decades. In the 1980s, knowledge-based techniques also made an impressive contribution to “intelligent” information retrieval and indexing. More recently, information science researchers have turned to other newer artificial-intelligence- based inductive learning techniques including neural networks, symbolic learning, and genetic algorithms. These newer techniques, which are grounded on diverse paradigms, have provided great opportunities for researchers to enhance the information processing and retrieval capabilities of current information storage and retrieval systems. In this article, we first provide an overview of these newer techniques and their use in information science research. To familiarize readers with these techniques, we present three popular methods: the connectionist Hopfield network; the symbolic ID3/ID5R; and evolution- based genetic algorithms. We discuss their knowledge representations and algorithms in the context of information retrieval. Sample implementation and testing results from our own research are also provided for each technique. We believe these techniques are promising in their ability to analyze user queries, identify users’ information needs, and suggest alternatives for search. With proper user-system interactions, these methods can greatly complement the prevailing full-text, keywordbased, probabilistic, and knowledge-based techniques.
Tang, Zibin. "A new design approach for numeric-to-symbolic conversion using neural networks." PDXScholar, 1991. https://pdxscholar.library.pdx.edu/open_access_etds/4242.
Повний текст джерелаCarmantini, Giovanni Sirio. "Dynamical systems theory for transparent symbolic computation in neuronal networks." Thesis, University of Plymouth, 2017. http://hdl.handle.net/10026.1/8647.
Повний текст джерелаBorges, Rafael. "A neural-symbolic system for temporal reasoning with application to model verification and learning." Thesis, City University London, 2012. http://openaccess.city.ac.uk/1303/.
Повний текст джерелаPollack, Courtney. "More Than Just Symbols: Mental and Neural Representations Related to Symbolic Number Processing in Mathematics." Thesis, Harvard University, 2016. http://nrs.harvard.edu/urn-3:HUL.InstRepos:27112714.
Повний текст джерелаCheng, Xiaoyu. "Applications of Artificial Neural Networks (ANNs) in exploring materials property-property correlations." Thesis, Queen Mary, University of London, 2014. http://qmro.qmul.ac.uk/xmlui/handle/123456789/7968.
Повний текст джерелаBorges, Rafael Vergara. "Investigações sobre raciocínio e aprendizagem temporal em modelos conexionistas." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2007. http://hdl.handle.net/10183/11488.
Повний текст джерелаComputational Intelligence is considered, by di erent authors in present days, the manifest destiny of Computer Science. The modelling of di erent aspects of cognition, such as learning and reasoning, has been a motivation for the integrated development of the symbolic and connectionist paradigms of artificial intelligence. More recently, such integration has led to the construction of models catering for integrated learning and reasoning. The integration of a temporal dimension into such systems is a relevant task as it allows for a richer representation of cognitive behaviour features, since time is considered an essential component in intelligent systems development. This work introduces SCTL (Sequential Connectionist Temporal Logic), a neuralsymbolic approach for integrating temporal knowledge, represented as logic programs, into recurrent neural networks. This integration is done in such a way that the semantic characterization of both representations are equivalent. Besides the strategy to achieve translation from one representation to another, and verification of the semantic equivalence, we also compare the proposed approach to other systems that perform symbolic and temporal representation in neural networks. Moreover, we describe the intended behaviour of the generated neural networks, for both temporal inference and learning through an algorithmic approach. Such behaviour is then evaluated by means several experiments, in order to analyse the performance of the model in cognitive modelling under di erent conditions and applications.
Kaithi, Bhargavacharan Reddy. "Knowledge Graph Reasoning over Unseen RDF Data." Wright State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=wright1571955816559707.
Повний текст джерелаGalassi, Andrea. "Symbolic versus sub-symbolic approaches: a case study on training Deep Networks to play Nine Men’s Morris game." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/12859/.
Повний текст джерелаLindberg, Maja. "The innate ability to cope with mathematics : A comparative fMRI study of children's and adults' neural activity during non-symbolic mathematical tasks." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158199.
Повний текст джерелаKissner, Michael [Verfasser], Helmut [Akademischer Betreuer] Mayer, Helmut [Gutachter] Mayer, and Martin [Gutachter] Werner. "A Neural-Symbolic Framework for Mental Simulation / Michael Kissner ; Gutachter: Helmut Mayer, Martin Werner ; Akademischer Betreuer: Helmut Mayer ; Universität der Bundeswehr München, Fakultät für Informatik." Neubiberg : Universitätsbibliothek der Universität der Bundeswehr München, 2020. http://d-nb.info/1223995933/34.
Повний текст джерелаBlanc, Jean-luc. "Transmission de l'information et complexité des activités de populations neuronales." Thesis, Aix-Marseille, 2012. http://www.theses.fr/2012AIXM4720/document.
Повний текст джерелаIn this thesis, we address the problem of transmission and information processing by neuronal assemblies, in terms of the interdisciplinary approach of complex systems by referring mainly to the formalisms of information theory and dynamical systems. In this context, we focus on the mechanisms underlying sensory information representation by neuronal activity through neural coding. We explore the structure of this code under several scales through the study of different neuronal population electrophysiological signals (singel unit, LFP and EEG). We have implemented various indices in order to extract objectively information from neural activity, but also to characterize the underlying dynamics from finite size time series (the entropy rate). We also defined a new indicator (the mutual information rate), which quantifies self-organization and relations of coupling between two systems. Using theoretical and numerical approaches, we analyze some characteristic properties of these indices and propose their use in the context of the study of neural systems. This work allows us to characterize the complexity of different neuronal activity associated to information transmission dynamics
Mundy, Darren Paul. "Using a symbolic algorithm to extract rules from connectionist networks." Thesis, University of Exeter, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.240400.
Повний текст джерелаMilaré, Claudia Regina. ""Extração de conhecimento de redes neurais artificiais utilizando sistemas de aprendizado simbólico e algoritmos genéticos"." Universidade de São Paulo, 2003. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-11082004-004358/.
Повний текст джерелаIn Machine Learning - ML there is not a single algorithm that is the best for all application domains. In practice, several research works have shown that Artificial Neural Networks - ANNs have an appropriate inductive bias for several domains. Thus, ANNs have been applied to a number of data sets with high predictive accuracy. Symbolic ML algorithms have a less flexible inductive bias than ANNs. While ANNs can learn any input-output mapping, i.e., ANNs have the universal approximation property, symbolic ML algorithms frequently learn concepts describing them using hyperplanes. On the other hand, symbolic algorithms are needed when a good understating of the decision process is essential, since symbolic ML algorithms express the knowledge induced using symbolic structures that can be interpreted and understood by humans. ANNs lack the capability of explaining their decisions since the knowledge is encoded as real-valued weights and biases of the network. This encoding is difficult to be interpreted by humans. In several application domains, such as credit approval and medical diagnosis, providing an explanation related to the classification given to a certain case is of crucial importance. In a similar way, several users of ML algorithms desire to validate the knowledge induced, in order to assure that the generalization made by the algorithm is correct. In order to apply ANNs to a larger number of application domains, several researches have proposed methods to extract comprehensible knowledge from ANNs. The primary contribution of this thesis consists of two methods that extract symbolic knowledge, expressed as decision rules, from ANNs. The proposed methods have several advantages over previous methods, such as being applicable to any architecture and supervised learning algorithm of ANNs. The first method uses standard symbolic ML algorithm to extract knowledge from ANNs, and the second method extends the first method by combining the knowledge induced by several symbolic ML algorithms through the application of a Genetic Algorithm - GA. The proposed methods are experimentally analyzed in a number of application domains. Results show that both methods are capable to extract symbolic knowledge having high fidelity with trained ANNs. The proposed methods are compared with TREPAN, showing promising results. TREPAN is a well known method to extract knowledge from ANNs.
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.
Повний текст джерелаBernardo, Alexandre. "Features for the Classification and Clustering of Music in Symbolic Format." Master's thesis, Department of Informatics, University of Lisbon, 2008. http://hdl.handle.net/10451/13947.
Повний текст джерелаOsório, Fernando Santos. "Inss : un système hybride neuro-symbolique pour l'apprentissage automatique constructif." Grenoble INPG, 1998. https://tel.archives-ouvertes.fr/tel-00004899.
Повний текст джерелаVarious 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
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.
Повний текст джерелаBader, Sebastian [Verfasser]. "Neural-symbolic integration / eingereicht von Sebastian Bader." 2009. http://d-nb.info/1008183652/34.
Повний текст джерелаRibeiro, Manuel António de Melo Chinopa de Sousa. "Neural and Symbolic AI - mind the gap! Aligning Artificial Neural Networks and Ontologies." Master's thesis, 2020. http://hdl.handle.net/10362/113651.
Повний текст джерелаHostičková, Iva. "Vývoj paradigmat výzkumu umělé inteligence." Master's thesis, 2014. http://www.nusl.cz/ntk/nusl-332251.
Повний текст джерелаBernardo, Alexandre Miguel Entradas. "Features for the Classification and Clustering of Music in Symbolic Format." Master's thesis, 2008. http://hdl.handle.net/10451/14010.
Повний текст джерела