Bibliografías temáticas / Automaton inference

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Literatura académica sobre el tema "Automaton inference"

Autor: Grafiati
Publicado: 1 de junio de 2024
Última modificación: 31 de julio de 2025

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Artículos de revistas sobre el tema "Automaton inference"

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Richetin, M., and M. Naranjo. "Inference of Automata by dialectic learning." Robotica 3, no. 3 (1985): 159–63. http://dx.doi.org/10.1017/s0263574700009085.

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SUMMARYAn algorithm for the inference of the external behaviour model of an automaton is given. It uses a sequential learning procedure based on induction-contradiction-correction concepts. The induction is a generalization of relationships between automaton state properties, and the correction consists in a more and more accurate discrimination of the automaton state properties. These properties are defined from the input/output contradictory sequences which are discovered after the observed contradictions between successive predictions and observations.
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Wieczorek, Wojciech, Tomasz Jastrzab, and Olgierd Unold. "Answer Set Programming for Regular Inference." Applied Sciences 10, no. 21 (2020): 7700. http://dx.doi.org/10.3390/app10217700.

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We propose an approach to non-deterministic finite automaton (NFA) inductive synthesis that is based on answer set programming (ASP) solvers. To that end, we explain how an NFA and its response to input samples can be encoded as rules in a logic program. We then ask an ASP solver to find an answer set for the program, which we use to extract the automaton of the required size. We conduct a series of experiments on some benchmark sets, using the implementation of our approach. The results show that our method outperforms, in terms of CPU time, a SAT approach and other exact algorithms on all be
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Hasanbeig, Hosein, Natasha Yogananda Jeppu, Alessandro Abate, Tom Melham, and Daniel Kroening. "Symbolic Task Inference in Deep Reinforcement Learning." Journal of Artificial Intelligence Research 80 (July 23, 2024): 1099–137. http://dx.doi.org/10.1613/jair.1.14063.

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This paper proposes DeepSynth, a method for effective training of deep reinforcement learning agents when the reward is sparse or non-Markovian, but at the same time progress towards the reward requires achieving an unknown sequence of high-level objectives. Our method employs a novel algorithm for synthesis of compact finite state automata to uncover this sequential structure automatically. We synthesise a human-interpretable automaton from trace data collected by exploring the environment. The state space of the environment is then enriched with the synthesised automaton, so that the generat
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HÖGBERG, JOHANNA. "A randomised inference algorithm for regular tree languages." Natural Language Engineering 17, no. 2 (2011): 203–19. http://dx.doi.org/10.1017/s1351324911000064.

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AbstractWe present a randomised inference algorithm for regular tree languages. The algorithm takes as input two disjoint finite nonempty sets of trees 𝒫 and 𝒩 and outputs a nondeterministic finite tree automaton that accepts every tree in 𝒫 and rejects every tree in 𝒩. The output automaton typically represents a nontrivial generalisation of the examples given in 𝒫 and 𝒩. To obtain compact output automata, we use a heuristics similar to bisimulation minimisation. The algorithm has time complexity of $\ordo{\negsize \cdot \possize^2}$, where n𝒩 and n𝒫 are the size of 𝒩 and 𝒫, respectively. Expe
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Topper, 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.

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Planning in finite stochastic environments is canonically posed as a Markov decision process where the transition and reward structures are explicitly known. Reinforcement learning (RL) lifts the explicitness assumption by working with sampling models instead. Further, with the advent of reward machines, we can relax the Markovian assumption on the reward. Angluin's active grammatical inference algorithm L* has found novel application in explicating reward machines for non-Markovian RL. We propose maintaining the assumption of explicit transition dynamics, but with an implicit non-Markovian re
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Di, Chong, Fangqi Li, Shenghong Li, and Jianwei Tian. "Bayesian inference based learning automaton scheme in Q-model environments." Applied Intelligence 51, no. 10 (2021): 7453–68. http://dx.doi.org/10.1007/s10489-021-02230-8.

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CHTOUROU, 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 (1997): 889–96. http://dx.doi.org/10.1080/00207729708929451.

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Senthil Kumar, K., and D. Malathi. "Context Free Grammar Identification from Positive Samples." International Journal of Engineering & Technology 7, no. 3.12 (2018): 1096. http://dx.doi.org/10.14419/ijet.v7i3.12.17768.

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In grammatical inference one aims to find underlying grammar or automaton which explains the target language in some way. Context free grammar which represents type 2 grammar in Chomsky hierarchy has many applications in Formal Language Theory, pattern recognition, Speech recognition, Machine learning , Compiler design and Genetic engineering etc. Identification of unknown Context Free grammar of the target language from positive examples is an extensive area in Grammatical Inference/ Grammar induction. In this paper we propose a novel method which finds the equivalent Chomsky Normal form.
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Grachev, 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.

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Intoduction: The regular inference problem is to synthesize deterministic finite-state automata by a list of words which are examplesand counterexamples of some unknown regular language. This problem is one of the main in the theory of formal languages and relatedfields. One of the most successful solutions to this problem is training a recurrent neural network on word classification and clusteringthe vectors in the space of RNN inner weights. However, it is not guaranteed that a consistent automaton can be constructed based onthe clustering results. More complex models require more memory, tr
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Kosala, 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 (2006): 129–58. http://dx.doi.org/10.1016/j.datak.2005.05.002.

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Tesis sobre el tema "Automaton inference"

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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.

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In this paper, we explore from an experimental point of view the possibilities and limitations of the new HYCGE learning algorithm for hybrid automata. As an example of a practical application, we study the algorithm’s performance on learning the behaviour of the action potential in excitable cells, specifically the Hodgkin-Huxley model of a squid giant axon, the Luo-Rudy model of a guinea pig ventricular cell, and the Entcheva model of a neonatal rat ventricular cell. The validity and accuracy of the algorithm is also visualized through graphical means.<br>I denna uppsats undersöker vi från e
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Rasoamanana, Aina Toky. "Derivation and Analysis of Cryptographic Protocol Implementation." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAS005.

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TLS et SSH sont deux protocoles de sécurité très répandu et étudiés par la communauté de la recherche. Dans cette thèse, nous nous concentrons sur une classe spécifique de vulnérabilités affectant les implémentations TLS et SSH, tels que les problèmes de machine à états. Ces vulnérabilités sont dues par des différences d'interprétation de la norme et correspondent à des écarts par rapport aux spécifications, par exemple l'acceptation de messages non valides ou l'acceptation de messages valides hors séquence.Nous développons une méthodologie généralisée et systématique pour déduire les machines
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Gransden, Thomas Glenn. "Automating proofs with state machine inference." Thesis, University of Leicester, 2017. http://hdl.handle.net/2381/40814.

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Interactive theorem provers are tools that help to produce formal proofs in a semiautomatic fashion. Originally designed to verify mathematical statements, they can be potentially useful in an industrial context. Despite being endorsed by leading mathematicians and computer scientists, these tools are not widely used. This is mainly because constructing proofs requires a large amount of human effort and knowledge. Frustratingly, there is limited proof automation available in many theorem proving systems. To address this limitation, a new technique called SEPIA (Search for Proofs Using Inferred
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Paige, 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.

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Probabilistic models used in quantitative sciences have historically co-evolved with methods for performing inference: specific modeling assumptions are made not because they are appropriate to the application domain, but because they are required to leverage existing software packages or inference methods. The intertwined nature of modeling and computational concerns leaves much of the promise of probabilistic modeling out of reach for data scientists, forcing practitioners to turn to off-the-shelf solutions. The emerging field of probabilistic programming aims to reduce the technical and cog
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MERINO, 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.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO<br>PROGRAMA DE EXCELENCIA ACADEMICA<br>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 co
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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.

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Imagine a world where computational simulations can be inverted as easily as running them forwards, where data can be used to refine models automatically, and where the only expertise one needs to carry out powerful statistical analysis is a basic proficiency in scientific coding. Creating such a world is the ambitious long-term aim of probabilistic programming. The bottleneck for improving the probabilistic models, or simulators, used throughout the quantitative sciences, is often not an ability to devise better models conceptually, but a lack of expertise, time, or resources to realize such
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Dixon, 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.

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Thesis (Ph. D.)--University of Oregon, 2004.<br>Typescript. 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.
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Losekoot, Théo. "Automatic program verification by inference of relational models." Electronic Thesis or Diss., Université de Rennes (2023-....), 2024. http://www.theses.fr/2024URENS102.

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Cette thèse porte sur la preuve automatique de propriétés concernant la relation entrée/sortie de programmes fonctionnels manipulant des types de données algébriques (ADT). De récents résultats montrent comment approximer un programme fonctionnel en utilisant un automate d'arbre. Bien qu'expressives, ces techniques ne peuvent pas prouver de propriété reliant l'entrée et la sortie d'une fonction, par exemple qu'inverser une liste préserve sa longueur. Dans cette thèse, nous nous appuyons sur ces résultats et définissons une procédure pour calculer ou sur-approximer une telle relation. Formellem
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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.

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Lin, Ye. "Internet data extraction based on automatic regular expression inference." [Ames, Iowa : Iowa State University], 2007.

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Libros sobre el tema "Automaton inference"

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Lee, Won Don. Probabilistic inference. Dept. of Computer Science, University of Illinois at Urbana-Champaign, 1986.

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Lee, Won Don. Probabilistic inference: Theory and practice. Dept. of Computer Science, University of Illinois at Urbana-Champaign, 1986.

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author, Kohlas Jürg 1939, ed. Generic Inference: A Unifying Theory for Automated Reasoning. Wiley, 2011.

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Farreny, Henri. AI and expertise: Heuristic search, inference engines, automatic proving. E. Horwood, 1989.

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Varlamov, Oleg. Fundamentals of creating MIVAR expert systems. INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1513119.

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Methodological and applied issues of the basics of creating knowledge bases and expert systems of logical artificial intelligence are considered. The software package "MIV Expert Systems Designer" (KESMI) Wi!Mi RAZUMATOR" (version 2.1), which is a convenient tool for the development of intelligent information systems. Examples of creating mivar expert systems and several laboratory works are given. The reader, having studied this tutorial, will be able to independently create expert systems based on KESMI. &#x0D; The textbook in the field of training "Computer Science and Computer Engineering"
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Varlamov, Oleg. Mivar databases and rules. INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1508665.

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The multidimensional open epistemological active network MOGAN is the basis for the transition to a qualitatively new level of creating logical artificial intelligence. Mivar databases and rules became the foundation for the creation of MOGAN. The results of the analysis and generalization of data representation structures of various data models are presented: from relational to "Entity — Relationship" (ER-model). On the basis of this generalization, a new model of data and rules is created: the mivar information space "Thing-Property-Relation". The logic-computational processing of data in th
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Higuera, Colin De La. Grammatical Inference: Learning Automata and Grammars. Cambridge University Press, 2010.

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Higuera, Colin de la. Grammatical Inference: Learning Automata and Grammars. Cambridge University Press, 2010.

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Higuera, Colin de la. Grammatical Inference: Learning Automata and Grammars. Cambridge University Press, 2014.

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Higuera, Colin de la. Grammatical Inference: Learning Automata and Grammars. Cambridge University Press, 2010.

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Capítulos de libros sobre el tema "Automaton inference"

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Dupont, Pierre, and Lin Chase. "Using symbol clustering to improve probabilistic automaton inference." In Grammatical Inference. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0054079.

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Firoiu, Laura, Tim Oates, and Paul R. Cohen. "Learning a deterministic finite automaton with a recurrent neural network." In Grammatical Inference. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0054067.

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Bessone, Nicolas, Payam Zahadat, and Kasper Stoy. "Neural Cellular Automaton for Decentralized Inference in Distributed Manipulation Systems." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70415-4_6.

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Krumnow, Arne, Swantje Plambeck, and Goerschwin Fey. "Using Forest Structures for Passive Automata Learning." In Machine Learning for Cyber-Physical Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-47062-2_7.

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AbstractWe present an extension on a passive learning algorithm for deterministic finite automata (DFAs) and Mealy machines which is based on the regular positive and negative inference (RPNI) algorithm. The extension builds a structure which is inspired by random forests. Instead of building a single automaton, the forest builds several. The paper introduces two forest structures for learning DFAs, together with their respective extension for Mealy machines, where the choice of which to pick depends on the output to achieve, as well as the type of application. Following that, the empirical an
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Xu, 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. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-84060-0_8.

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Hajdu, Márton, Laura Kovács, Michael Rawson, and Andrei Voronkov. "Reducibility Constraints in Superposition." In Automated Reasoning. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-63498-7_8.

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AbstractModern superposition inference systems aim at reducing the search space by introducing redundancy criteria on clauses and inferences. This paper focuses on reducing the number of superposition inferences with a single clause by blocking inferences into some terms, provided there were previously made inferences of a certain form performed with predecessors of this clause. Other calculi based on blocking inferences, for example basic superposition, rely on variable abstraction or equality constraints to express irreducibility of terms, resulting however in blocking inferences with all su
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Yang, Hui, Yue Ma, and Nicole Bidoit. "Hypergraph-Based Inference Rules for Computing $$\mathcal{EL}\mathcal{}^+$$-Ontology Justifications." In Automated Reasoning. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10769-6_19.

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AbstractTo give concise explanations for a conclusion obtained by reasoning over ontologies, justifications have been proposed as minimal subsets of an ontology that entail the given conclusion. Even though computing one justification can be done in polynomial time for tractable Description Logics such as $$\mathcal{EL}\mathcal{}^+$$ EL + , computing all justifications is complicated and often challenging for real-world ontologies. In this paper, based on a graph representation of $$\mathcal{EL}\mathcal{}^+$$ EL + -ontologies, we propose a new set of inference rules (called H-rules) and take a
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Newborn, Monty. "Inference Procedures." In Automated Theorem Proving. Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4613-0089-2_4.

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Bhayat, Ahmed, Johannes Schoisswohl, and Michael Rawson. "Superposition with Delayed Unification." In Automated Deduction – CADE 29. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-38499-8_2.

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AbstractClassically, in saturation-based proof systems, unification has been considered atomic. However, it is also possible to move unification to the calculus level, turning the steps of the unification algorithm into inferences. For calculi that rely on unification procedures returning large or even infinite sets of unifiers, integrating unification into the calculus is an attractive method of dovetailing unification and inference. This applies, for example, to AC-superposition and higher-order superposition. We show that first-order superposition remains complete when moving unification ru
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de la Higuera, Colin. "Learning stochastic finite automata from experts." In Grammatical Inference. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0054066.

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Actas de conferencias sobre el tema "Automaton inference"

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da Silva, Brivaldo Alves, Ítalo Cunha, and Ronaldo A. Ferreira. "Automatic Inference of BGP Community Semantics." In NOMS 2025-2025 IEEE Network Operations and Management Symposium. IEEE, 2025. https://doi.org/10.1109/noms57970.2025.11073643.

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Loh, Randolph, Zhongmin Dai, Kar Wai Fok, and Vrizlynn L. L. Thing. "Automated Protocol Reverse Engineering with Message Format Inference." In 2024 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT). IEEE, 2024. https://doi.org/10.1109/icmlant63295.2024.00022.

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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.

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Grantner, 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.

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Saika, 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.

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Asami, 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.

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Xu, Zhe, and Ufuk Topcu. "Transfer of Temporal Logic Formulas in Reinforcement Learning." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/557.

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Transferring high-level knowledge from a source task to a target task is an effective way to expedite reinforcement learning (RL). For example, propositional logic and first-order logic have been used as representations of such knowledge. We study the transfer of knowledge between tasks in which the timing of the events matters. We call such tasks temporal tasks. We concretize similarity between temporal tasks through a notion of logical transferability, and develop a transfer learning approach between different yet similar temporal tasks. We first propose an inference technique to extract met
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Bhoyar, 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. IMarEST, 2019. http://dx.doi.org/10.24868/icmet.oman.2019.008.

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Marine vessels have been recently considered for redesign with a view towards autonomous operation. This brings forth a number of safety concerns as regards malware attacks on intra-vehicle communications systems as well as on sensor based communication with their environment. Designing suitable hybrid systems or cyber physical systems as the above, which are data driven, involves a challenge by way of difficulty in abstraction. The current modeling paradigm for cyber physical systems is based upon the abstract idea of a hybrid automaton which involves discrete as well as continuous mathematic
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Li, Z., N. Mahajan, A. Mathur, and C. Koritala. "A Robust Methodology for Rig Equipment Automation Leveraging Computer Vision." In SPE/IADC International Drilling Conference and Exhibition. SPE, 2025. https://doi.org/10.2118/223688-ms.

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Abstract Introducing an advanced artificial intelligence (AI) system leveraging computer vision technology to automate rig operations in the oil and gas industry. Our approach uniquely addresses the challenges of deploying computer vision technology in harsh rig environments, ensuring consistent operation under variable conditions. The system reduces manual labor, enhances safety, and improves operational efficiency with a 95% accuracy rate. Tested over two years across diverse geographical locations, it demonstrates robust performance. We detail the process of data preprocessing and construct
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Pastore, 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.

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Informes sobre el tema "Automaton inference"

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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.

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In Description Logics (DLs), both tableau-based and automatabased algorithms are frequently used to show decidability and complexity results for basic inference problems such as satisfiability of concepts. Whereas tableau-based algorithms usually yield worst-case optimal algorithms in the case of PSpace-complete logics, it is often very hard to design optimal tableau-based algorithms for ExpTime-complete DLs. In contrast, the automata-based approach is usually well-suited to prove ExpTime upper-bounds, but its direct application will usually also yield an ExpTime-algorithm for a PSpace-complet
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2

Baader, Franz, and Benjamin Zarrieß. Verification of Golog Programs over Description Logic Actions. Technische Universität Dresden, 2013. http://dx.doi.org/10.25368/2022.198.

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High-level action programming languages such as Golog have successfully been used to model the behavior of autonomous agents. In addition to a logic-based action formalism for describing the environment and the effects of basic actions, they enable the construction of complex actions using typical programming language constructs. To ensure that the execution of such complex actions leads to the desired behavior of the agent, one needs to specify the required properties in a formal way, and then verify that these requirements are met by any execution of the program. Due to the expressiveness of
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Baader, 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.

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Although being quite inexpressive, the description logic (DL) FL₀, which provides only conjunction, value restriction and the top concept as concept constructors, has an intractable subsumption problem in the presence of terminologies (TBoxes): subsumption reasoning w.r.t. acyclic FL₀ TBoxes is coNP-complete, and becomes even ExpTime-complete in case general TBoxes are used. In the present paper, we use automata working on infinite trees to solve both standard and non-standard inferences in FL₀ w.r.t. general TBoxes. First, we give an alternative proof of the ExpTime upper bound for subsumptio
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4

Brown, Frank M. Automatic Inference in Quantified Computational Logic. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada200909.

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Pasupuleti, Murali Krishna. Stochastic Computation for AI: Bayesian Inference, Uncertainty, and Optimization. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv325.

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Abstract: Stochastic computation is a fundamental approach in artificial intelligence (AI) that enables probabilistic reasoning, uncertainty quantification, and robust decision-making in complex environments. This research explores the theoretical foundations, computational techniques, and real-world applications of stochastic methods, focusing on Bayesian inference, Monte Carlo methods, stochastic optimization, and uncertainty-aware AI models. Key topics include probabilistic graphical models, Markov Chain Monte Carlo (MCMC), variational inference, stochastic gradient descent (SGD), and Bayes
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6

Videa, Aldo, and Yiyi Wang. Inference of Transit Passenger Counts and Waiting Time Using Wi-Fi Signals. Western Transportation Institute, 2021. http://dx.doi.org/10.15788/1715288737.

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Passenger data such as real-time origin-destination (OD) flows and waiting times are central to planning public transportation services and improving visitor experience. This project explored the use of Internet of Things (IoT) Technology to infer transit ridership and waiting time at bus stops. Specifically, this study explored the use of Raspberry Pi computers, which are small and inexpensive sets of hardware, to scan the Wi-Fi networks of passengers’ smartphones. The process was used to infer passenger counts and obtain information on passenger trajectories based on Global Positioning Syste
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7

de Kemp, E. A., H. A. J. Russell, B. Brodaric, 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.

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Application of 3D technologies to the wide range of Geosciences knowledge domains is well underway. These have been operationalized in workflows of the hydrocarbon sector for a half-century, and now in mining for over two decades. In Geosciences, algorithms, structured workflows and data integration strategies can support compelling Earth models, however challenges remain to meet the standards of geological plausibility required for most geoscientific studies. There is also missing links in the institutional information infrastructure supporting operational multi-scale 3D data and model develo
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de Kemp, E. A., H. A. J. Russell, B. Brodaric, 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.

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Application of 3D technologies to the wide range of Geosciences knowledge domains is well underway. These have been operationalized in workflows of the hydrocarbon sector for a half-century, and now in mining for over two decades. In Geosciences, algorithms, structured workflows and data integration strategies can support compelling Earth models, however challenges remain to meet the standards of geological plausibility required for most geoscientific studies. There is also missing links in the institutional information infrastructure supporting operational multi-scale 3D data and model develo
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9

Burstein, Jill, Geoffrey LaFlair, Antony Kunnan, and Alina von Davier. A Theoretical Assessment Ecosystem for a Digital-First Assessment - The Duolingo English Test. Duolingo, 2022. http://dx.doi.org/10.46999/kiqf4328.

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The Duolingo English Test is a groundbreaking, digital­first, computer­adaptive measure of English language proficiency for communication and use in English­medium settings. The test measures four key English language proficiency constructs: Speaking, Writing, Reading, and Listening (SWRL), and is aligned with the Common European Framework of Reference for Languages (CEFR) proficiency levels and descriptors. As a digital­first assessment, the test uses “human­in­the­loop AI” from end to end for test security, automated item generation, and scoring of test­taker responses. This paper presents a
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Paule, 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.

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In the context of climate change and the environmental and energy constraints we face, it is essential to develop methods to encourage the implementation of efficient solutions for building renovation. One of the objectives of the European PRELUDE project [1] is to develop a "Building Renovation Roadmap"(BRR) aimed at facilitating decision-making to foster the most efficient refurbishment actions, the implementation of innovative solutions and the promotion of renewable energy sources in the renovation process of existing buildings. In this context, Estia is working on the development of infer
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