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Статті в журналах з теми "Automaton inference"
Richetin, M., and M. Naranjo. "Inference of Automata by dialectic learning." Robotica 3, no. 3 (September 1985): 159–63. http://dx.doi.org/10.1017/s0263574700009085.
Повний текст джерелаHÖGBERG, JOHANNA. "A randomised inference algorithm for regular tree languages." Natural Language Engineering 17, no. 2 (March 21, 2011): 203–19. http://dx.doi.org/10.1017/s1351324911000064.
Повний текст джерелаWieczorek, Wojciech, Tomasz Jastrzab, and Olgierd Unold. "Answer Set Programming for Regular Inference." Applied Sciences 10, no. 21 (October 30, 2020): 7700. http://dx.doi.org/10.3390/app10217700.
Повний текст джерелаGrachev, Petr, Sergey Muravyov, Andrey Filchenkov, and Anatoly Shalyto. "Automata generation based on recurrent neural networks and automated cauterization selection." Information and Control Systems, no. 1 (February 19, 2020): 34–43. http://dx.doi.org/10.31799/1684-8853-2020-1-34-43.
Повний текст джерелаTopper, Noah, George Atia, Ashutosh Trivedi, and Alvaro Velasquez. "Active Grammatical Inference for Non-Markovian Planning." Proceedings of the International Conference on Automated Planning and Scheduling 32 (June 13, 2022): 647–51. http://dx.doi.org/10.1609/icaps.v32i1.19853.
Повний текст джерелаDi, Chong, Fangqi Li, Shenghong Li, and Jianwei Tian. "Bayesian inference based learning automaton scheme in Q-model environments." Applied Intelligence 51, no. 10 (March 10, 2021): 7453–68. http://dx.doi.org/10.1007/s10489-021-02230-8.
Повний текст джерелаCHTOUROU, MOHAMED, MAHER BEN JEMAA, and RAOUF KETATA. "A learning-automaton-based method for fuzzy inference system identification." International Journal of Systems Science 28, no. 9 (July 1997): 889–96. http://dx.doi.org/10.1080/00207729708929451.
Повний текст джерелаSenthil Kumar, K., and D. Malathi. "Context Free Grammar Identification from Positive Samples." International Journal of Engineering & Technology 7, no. 3.12 (July 20, 2018): 1096. http://dx.doi.org/10.14419/ijet.v7i3.12.17768.
Повний текст джерелаKosala, Raymond, Hendrik Blockeel, Maurice Bruynooghe, and Jan Van den Bussche. "Information extraction from structured documents using k-testable tree automaton inference." Data & Knowledge Engineering 58, no. 2 (August 2006): 129–58. http://dx.doi.org/10.1016/j.datak.2005.05.002.
Повний текст джерелаTîrnăucă, Cristina. "A Survey of State Merging Strategies for DFA Identification in the Limit." Triangle, no. 8 (June 29, 2018): 121. http://dx.doi.org/10.17345/triangle8.121-136.
Повний текст джерелаДисертації з теми "Automaton inference"
Ansin, Rasmus, and Didrik Lundberg. "Automated Inference of Excitable Cell Models as Hybrid Automata." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-154065.
Повний текст джерелаI denna uppsats undersöker vi från en experimentell synvinkel möjligheter och begränsningar i den nya inlärningsalgoritmen HYCGE för hybridautomater. Som ett exempel på en praktisk tillämpning, studerar vi algoritmens förmåga att lära sig aktionspotentialens beteende i retbara celler, specifikt Hodgkin-Huxleymodellen av en bläckfisks jätteaxon, Luo-Rudymodellen av en ventrikulärcell i marsvin, och Entchevas modell av en ventrikulär cell i nyfödd råtta .Giltigheten och noggrannheten hos algoritmen visualiseras även genom grafiskamedel.
Rasoamanana, Aina Toky. "Derivation and Analysis of Cryptographic Protocol Implementation." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAS005.
Повний текст джерелаTLS and SSH are two well-known and thoroughly studied security protocols. In this thesis, we focus on a specific class of vulnerabilities affecting both protocols implementations, state machine errors. These vulnerabilities are caused by differences in interpreting the standard and correspond to deviations from the specifications, e.g. accepting invalid messages, or accepting valid messages out of sequence.We develop a generalized and systematic methodology to infer the protocol state machines such as the major TLS and SSH stacks from stimuli and observations, and to study their evolution across revisions. We use the L* algorithm to compute state machines corresponding to different execution scenarios.We reproduce several known vulnerabilities (denial of service, authentication bypasses), and uncover new ones. We also show that state machine inference is efficient and practical enough in many cases for integration within a continuous integration pipeline, to help find new vulnerabilities or deviations introduced during development.With our systematic black-box approach, we study over 600 different versions of server and client implementations in various scenarios (protocol versions, options). Using the resulting state machines, we propose a robust algorithm to fingerprint TLS and SSH stacks. To the best of our knowledge, this is the first application of this approach on such a broad perimeter, in terms of number of TLS and SSH stacks, revisions, or execution scenarios studied
Gransden, Thomas Glenn. "Automating proofs with state machine inference." Thesis, University of Leicester, 2017. http://hdl.handle.net/2381/40814.
Повний текст джерелаPaige, Timothy Brooks. "Automatic inference for higher-order probabilistic programs." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:d912c4de-4b08-4729-aa19-766413735e2a.
Повний текст джерелаMERINO, JORGE SALVADOR PAREDES. "AUTOMATIC SYNTHESIS OF FUZZY INFERENCE SYSTEMS FOR CLASSIFICATION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2015. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=27007@1.
Повний текст джерелаCOORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
PROGRAMA DE EXCELENCIA ACADEMICA
Hoje em dia, grande parte do conhecimento acumulado está armazenado em forma de dados. Para muitos problemas de classificação, tenta-se aprender a relação entre um conjunto de variáveis (atributos) e uma variável alvo de interesse. Dentre as ferramentas capazes de atuar como modelos representativos de sistemas reais, os Sistemas de Inferência Fuzzy são considerados excelentes com respeito à representação do conhecimento de forma compreensível, por serem baseados em regras linguísticas. Este quesito de interpretabilidade linguística é relevante em várias aplicações em que não se deseja apenas um modelo do tipo caixa preta, que, por mais precisão que proporcione, não fornece uma explicação de como os resultados são obtidos. Esta dissertação aborda o desenvolvimento de um Sistema de Inferência Fuzzy de forma automática, buscando uma base de regras que valorize a interpretabilidade linguística e que, ao mesmo tempo, forneça uma boa acurácia. Para tanto, é proposto o modelo AutoFIS-Class, um método automático para a geração de Sistemas de Inferência Fuzzy para problemas de classificação. As características do modelo são: (i) geração de premissas que garantam critérios mínimos de qualidade, (ii) associação de cada premissa a um termo consequente mais compatível e (iii) agregação de regras de uma mesma classe por meio de operadores que ponderem a influência de cada regra. O modelo proposto é avaliado em 45 bases de dados benchmark e seus resultados são comparados com modelos da literatura baseados em Algoritmos Evolucionários. Os resultados comprovam que o Sistema de Inferência gerado é competitivo, apresentando uma boa acurácia com um baixo número de regras.
Nowadays, much of the accumulated knowledge is stored as data. In many classification problems the relationship between a set of variables (attributes) and a target variable of interest must be learned. Among the tools capable of modeling real systems, Fuzzy Inference Systems are considered excellent with respect to the knowledge representation in a comprehensible way, as they are based on inference rules. This is relevant in applications where a black box model does not suffice. This model may attain good accuracy, but does not explain how results are obtained. This dissertation presents the development of a Fuzzy Inference System in an automatic manner, where the rule base should favour linguistic interpretability and at the same time provide good accuracy. In this sense, this work proposes the AutoFIS-Class model, an automatic method for generating Fuzzy Inference Systems for classification problems. Its main features are: (i) generation of premises to ensure minimum, quality criteria, (ii) association of each rule premise to the most compatible consequent term; and (iii) aggregation of rules for each class through operator that weigh the relevance of each rule. The proposed model was evaluated for 45 datasets and their results were compared to existing models based on Evolutionary Algorithms. Results show that the proposed Fuzzy Inference System is competitive, presenting good accuracy with a low number of rules.
Rainforth, Thomas William Gamlen. "Automating inference, learning, and design using probabilistic programming." Thesis, University of Oxford, 2017. http://ora.ox.ac.uk/objects/uuid:e276f3b4-ff1d-44bf-9d67-013f68ce81f0.
Повний текст джерелаDixon, Heidi. "Automating pseudo-Boolean inference within a DPLL framework /." view abstract or download file of text, 2004. http://wwwlib.umi.com/cr/uoregon/fullcit?p3153782.
Повний текст джерелаTypescript. Includes vita and abstract. Includes bibliographical references (leaves 140-146). Also available for download via the World Wide Web; free to University of Oregon users.
MacNish, Craig Gordon. "Nonmonotonic inference systems for modelling dynamic processes." Thesis, University of Cambridge, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.240195.
Повний текст джерелаLin, Ye. "Internet data extraction based on automatic regular expression inference." [Ames, Iowa : Iowa State University], 2007.
Знайти повний текст джерелаEl, Kaliouby Rana Ayman. "Mind-reading machines : automated inference of complex mental states." Thesis, University of Cambridge, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.615030.
Повний текст джерелаКниги з теми "Automaton inference"
Lee, Won Don. Probabilistic inference. Urbana, Ill: Dept. of Computer Science, University of Illinois at Urbana-Champaign, 1986.
Знайти повний текст джерелаLee, Won Don. Probabilistic inference: Theory and practice. Urbana, Ill: Dept. of Computer Science, University of Illinois at Urbana-Champaign, 1986.
Знайти повний текст джерелаauthor, Kohlas Jürg 1939, ed. Generic Inference: A Unifying Theory for Automated Reasoning. Hoboken, New Jersey: Wiley, 2011.
Знайти повний текст джерелаFarreny, Henri. AI and expertise: Heuristic search, inference engines, automatic proving. Chichester: E. Horwood, 1989.
Знайти повний текст джерелаVarlamov, Oleg. Fundamentals of creating MIVAR expert systems. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1513119.
Повний текст джерелаVarlamov, Oleg. Mivar databases and rules. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1508665.
Повний текст джерелаHiguera, Colin De La. Grammatical Inference: Learning Automata and Grammars. Cambridge University Press, 2010.
Знайти повний текст джерелаHiguera, Colin de la. Grammatical Inference: Learning Automata and Grammars. Cambridge University Press, 2010.
Знайти повний текст джерелаHiguera, Colin de la. Grammatical Inference: Learning Automata and Grammars. Cambridge University Press, 2014.
Знайти повний текст джерелаHiguera, Colin de la. Grammatical Inference: Learning Automata and Grammars. Cambridge University Press, 2010.
Знайти повний текст джерелаЧастини книг з теми "Automaton inference"
Dupont, Pierre, and Lin Chase. "Using symbol clustering to improve probabilistic automaton inference." In Grammatical Inference, 232–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0054079.
Повний текст джерелаFiroiu, Laura, Tim Oates, and Paul R. Cohen. "Learning a deterministic finite automaton with a recurrent neural network." In Grammatical Inference, 90–101. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0054067.
Повний текст джерелаXu, Zhe, Bo Wu, Aditya Ojha, Daniel Neider, and Ufuk Topcu. "Active Finite Reward Automaton Inference and Reinforcement Learning Using Queries and Counterexamples." In Lecture Notes in Computer Science, 115–35. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-84060-0_8.
Повний текст джерелаYang, Hui, Yue Ma, and Nicole Bidoit. "Hypergraph-Based Inference Rules for Computing $$\mathcal{EL}\mathcal{}^+$$-Ontology Justifications." In Automated Reasoning, 310–28. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10769-6_19.
Повний текст джерелаNewborn, Monty. "Inference Procedures." In Automated Theorem Proving, 29–42. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4613-0089-2_4.
Повний текст джерелаBhayat, Ahmed, Johannes Schoisswohl, and Michael Rawson. "Superposition with Delayed Unification." In Automated Deduction – CADE 29, 23–40. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-38499-8_2.
Повний текст джерелаde la Higuera, Colin. "Learning stochastic finite automata from experts." In Grammatical Inference, 79–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0054066.
Повний текст джерелаViechnicki, Peter. "A performance evaluation of automatic survey classifiers." In Grammatical Inference, 244–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0054080.
Повний текст джерелаStickel, Mark E. "PTTP and Linked Inference." In Automated Reasoning Series, 283–95. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3488-0_14.
Повний текст джерелаStachniak, Zbigniew. "Nonmonotonic Resolution Inference Systems." In Automated Reasoning Series, 165–78. Dordrecht: Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-009-1677-7_8.
Повний текст джерелаТези доповідей конференцій з теми "Automaton inference"
Zhaohua, Huang, and Yang Fan. "Information Extraction from Web Documents Based on Unranked Tree Automaton Inference." In 2012 4th International Conference on Multimedia Information Networking and Security (MINES). IEEE, 2012. http://dx.doi.org/10.1109/mines.2012.128.
Повний текст джерелаGrantner, Janos L., Sean T. Fuller, and Jozsef Dombi. "Fuzzy automaton model with adaptive inference mechanism for intelligent decision support systems." In 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2016. http://dx.doi.org/10.1109/fuzz-ieee.2016.7737991.
Повний текст джерелаSaika, Yohei, Shouta Akiyama, and Hiroki Sakaematsu. "Bayesian inference in optical measurement due to remote sensing to synthetic aperture radar interferometry." In 2013 13th International Conference on Control, Automaton and Systems (ICCAS). IEEE, 2013. http://dx.doi.org/10.1109/iccas.2013.6704157.
Повний текст джерелаAsami, Atsushi, Tatsuki Yamada, and Yohei Saika. "Probabilistic inference of environmental factors via time series analysis using mean-field theory of ising model." In 2013 13th International Conference on Control, Automaton and Systems (ICCAS). IEEE, 2013. http://dx.doi.org/10.1109/iccas.2013.6704168.
Повний текст джерелаXu, Zhe, and Ufuk Topcu. "Transfer of Temporal Logic Formulas in Reinforcement Learning." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/557.
Повний текст джерелаBhoyar, A., S. Sharma, S. Barve, and R. Kumar Rana. "Intelligent Control of Autonomous Vessels: Bayesian Estimation Instead of Statistical Learning?" In International Conference on Marine Engineering and Technology Oman. London: IMarEST, 2019. http://dx.doi.org/10.24868/icmet.oman.2019.008.
Повний текст джерелаPastore, Fabrizio, Daniela Micucci, and Leonardo Mariani. "Timed k-Tail: Automatic Inference of Timed Automata." In 2017 IEEE International Conference on Software Testing, Verification and Validation (ICST). IEEE, 2017. http://dx.doi.org/10.1109/icst.2017.43.
Повний текст джерелаByrne, Ruth M. J. "Good Explanations in Explainable Artificial Intelligence (XAI): Evidence from Human Explanatory Reasoning." 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.2023/733.
Повний текст джерелаDeb, Sankha, and Kalyan Ghosh. "Artificial Intelligence Based Inference Techniques for Automated Process Planning for Machined Parts." In ASME 2002 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2002. http://dx.doi.org/10.1115/detc2002/cie-34507.
Повний текст джерелаEichhoff, Julian R., Felix Baumann, and Dieter Roller. "Two Approaches to the Induction of Graph-Rewriting Rules for Function-Based Design Synthesis." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-59915.
Повний текст джерелаЗвіти організацій з теми "Automaton inference"
Baader, Franz, Jan Hladik, and Rafael Peñaloza. PSpace Automata with Blocking for Description Logics. Aachen University of Technology, 2006. http://dx.doi.org/10.25368/2022.157.
Повний текст джерелаBaader, Franz, and Benjamin Zarrieß. Verification of Golog Programs over Description Logic Actions. Technische Universität Dresden, 2013. http://dx.doi.org/10.25368/2022.198.
Повний текст джерелаBaader, Franz, Oliver Fernández Gil, and Maximilian Pensel. Standard and Non-Standard Inferences in the Description Logic FL₀ Using Tree Automata. Technische Universität Dresden, 2018. http://dx.doi.org/10.25368/2022.240.
Повний текст джерелаBrown, Frank M. Automatic Inference in Quantified Computational Logic. Fort Belvoir, VA: Defense Technical Information Center, October 1988. http://dx.doi.org/10.21236/ada200909.
Повний текст джерелаVidea, Aldo, and Yiyi Wang. Inference of Transit Passenger Counts and Waiting Time Using Wi-Fi Signals. Western Transportation Institute, August 2021. http://dx.doi.org/10.15788/1715288737.
Повний текст джерелаde Kemp, E. A., H. A. J. Russell, B. Brodaric, D. B. Snyder, M. J. Hillier, M. St-Onge, C. Harrison, et al. Initiating transformative geoscience practice at the Geological Survey of Canada: Canada in 3D. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/331097.
Повний текст джерелаde Kemp, E. A., H. A. J. Russell, B. Brodaric, D. B. Snyder, M. J. Hillier, M. St-Onge, C. Harrison, et al. Initiating transformative geoscience practice at the Geological Survey of Canada: Canada in 3D. Natural Resources Canada/CMSS/Information Management, 2023. http://dx.doi.org/10.4095/331871.
Повний текст джерелаBurstein, Jill, Geoffrey LaFlair, Antony Kunnan, and Alina von Davier. A Theoretical Assessment Ecosystem for a Digital-First Assessment - The Duolingo English Test. Duolingo, March 2022. http://dx.doi.org/10.46999/kiqf4328.
Повний текст джерелаPaule, Bernard, Flourentzos Flourentzou, Tristan de KERCHOVE d’EXAERDE, Julien BOUTILLIER, and Nicolo Ferrari. PRELUDE Roadmap for Building Renovation: set of rules for renovation actions to optimize building energy performance. Department of the Built Environment, 2023. http://dx.doi.org/10.54337/aau541614638.
Повний текст джерелаDeep learning for individual heterogeneity: an automatic inference framework. Cemmap, July 2021. http://dx.doi.org/10.47004/wp.cem.2021.2921.
Повний текст джерела