Letteratura scientifica selezionata sul tema "Regular Grammar Induction"
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Articoli di riviste sul tema "Regular Grammar Induction":
Liu, Linlin. "English Pedagogical Grammar: Teaching Present Perfect and Present Perfect Continuous by Deductive and Inductive Approaches". Studies in English Language Teaching 8, n. 3 (22 agosto 2020): p138. http://dx.doi.org/10.22158/selt.v8n3p138.
-, Dr S. Ansar Hussain, e Dr R. V. Jayanth Kasyap -. "Tools of Language Learning - A Pedagogical Perspective". International Journal For Multidisciplinary Research 6, n. 1 (29 febbraio 2024). http://dx.doi.org/10.36948/ijfmr.2024.v06i01.11595.
Tesi sul tema "Regular Grammar Induction":
Grand, Maxence. "Apprentissage de Modèle d'Actions basé sur l'Induction Grammaticale Régulière pour la Planification en Intelligence Artificielle". Electronic Thesis or Diss., Université Grenoble Alpes, 2022. http://www.theses.fr/2022GRALM044.
The field of artificial intelligence aims to design and build autonomous agents able to perceive, learn and act without any human intervention to perform complex tasks. To perform complex tasks, the autonomous agent must plan the best possible actions and execute them. To do this, the autonomous agent needs an action model. An action model is a semantic representation of the actions it can execute. In an action model, an action is represented using (1) a precondition: the set of conditions that must be satisfied for the action to be executed and (2) the effects: the set of properties of the world that will be altered by the execution of the action. STRIPS planning is a classical method to design these action models. However, STRIPS action models are generally too restrictive to be used in real-world applications. There are other forms of action models: temporal action models allowing to represent actions that can be executed concurrently, HTN action models allowing to represent actions as tasks and subtasks, etc. These models are less restrictive, but the less restrictive the models are the more difficult they are to design. In this thesis, we are interested in approaches facilitating the acquisition of these action models based on machine learning techniques.In this thesis, we present AMLSI (Action Model Learning with State machine Interaction), an approach for action model learning based on Regular Grammatical Induction. First, we show that the AMLSI approach allows to learn (STRIPS) action models. We will show the different properties of the approach proving its efficiency: robustness, convergence, require few learning data, quality of the learned models. In a second step, we propose two extensions for temporal action model learning and HTN action model learning
Packer, Thomas L. "Scalable Detection and Extraction of Data in Lists in OCRed Text for Ontology Population Using Semi-Supervised and Unsupervised Active Wrapper Induction". BYU ScholarsArchive, 2014. https://scholarsarchive.byu.edu/etd/4258.
Gebhardt, Kilian. "Induction, Training, and Parsing Strategies beyond Context-free Grammars". 2019. https://tud.qucosa.de/id/qucosa%3A71398.
Capitoli di libri sul tema "Regular Grammar Induction":
Unold, Olgierd. "Regular Language Induction with Grammar-based Classifier System". In Engineering the Computer Science and IT. InTech, 2009. http://dx.doi.org/10.5772/7768.
Atti di convegni sul tema "Regular Grammar Induction":
Belcak, Peter, David Hofer e Roger Wattenhofer. "A Neural Model for Regular Grammar Induction". In 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2022. http://dx.doi.org/10.1109/icmla55696.2022.00064.