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4 results on '"Dignum, V"'

1. Coordination, Organization, Institutions and Norms in Agent Systems II, AAMAS 2006 and ECAI 2006 International Workshops, Revised Selected Papers

Author

Noriega, P., Vázquez-Salceda, J., Boella, G., Boissier, Olivier, Dignum, V., Fornara, N., Matson, E., Département Systèmes Multi-Agents (SMA-ENSMSE), École des Mines de Saint-Étienne (Mines Saint-Étienne MSE), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Centre G2I, P. Noriega, J. Vasquez-Salceda, G. Boella, O. Boissier, V. Dignum, N. Fornara, and E. Matson

Abstract

Series: Lecture Notes in Computer Science, Vol. 4386 Subseries: Lecture Notes in Artificial Intelligence Noriega, P.; Vázquez-Salceda, J.; Boella, G.; Boissier, O.; Dignum, V.; Fornara, N.; Matson, E. (Eds.); International audience

Published
2007

2. The Advanced Course on Human-Centered AI: Learning Objectives

Author

Mohamed Chetouani, Virginia Dignum, Paul Lukowicz, Carles Sierra, Sorbonne Université (SU), Institut des Systèmes Intelligents et de Robotique (ISIR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Perception, Interaction, Robotique sociales (PIROS), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Umeå University, Deutsches Forschungszentrum für Künstliche Intelligenz GmbH = German Research Center for Artificial Intelligence (DFKI), Artificial Intelligence Research Institute / Spanish Scientific Research Council (IIIA / CSIC), Universitat Autònoma de Barcelona (UAB), Chetouani, M., Dignum, V., Lukowicz, P., Sierra, C., and European Project: 952026,EC:H2020 | RIA,HumanE AI Network(2020)

Subjects
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience

Published
2023
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3. Interactive Robot Learning: An Overview

Author

Mohamed Chetouani, Sorbonne Université (SU), Institut des Systèmes Intelligents et de Robotique (ISIR), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Perception, Interaction, Robotique sociales (PIROS), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Chetouani, M., Dignum, V., Lukowicz, P., Sierra, C., European Project: 765955,ANIMATAS, and European Project: 952026,EC:H2020 | RIA,HumanE AI Network(2020)

Subjects
[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience

Published
2023
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4. Relational Grounded Language Learning

Author

Becerra-Bonache, Leonor, Blockeel, Hendrik, Galvan, Maria, Jacquenet, François, Laboratoire Hubert Curien [Saint Etienne] (LHC), Institut d'Optique Graduate School (IOGS)-Université Jean Monnet [Saint-Étienne] (UJM)-Centre National de la Recherche Scientifique (CNRS), Declarative Languages and Artificial Intelligence (DTAI), Université Catholique de Louvain = Catholic University of Louvain (UCL), Catholic University of Leuven - Katholieke Universiteit Leuven (KU Leuven), Kaminka, GA, Fox, M, Bouquet, P, Hullermeier, E, Dignum, V, Dignum, F, and VanHarmelen, F

Subjects
machine learning, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], natural language learning, inductive logic programming, [INFO.INFO-LO]Computer Science [cs]/Logic in Computer Science [cs.LO], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

In the past, research on learning language models mainly used syntactic information during the learning process but in recent years, researchers began to also use semantic information. This paper presents such an approach where the input of our learning algorithm is a dataset of pairs made up of sentences and the contexts in which they are produced. The system we present is based on inductive logic programming techniques that aim to learn a mapping between n-grams and a semantic representation of their associated meaning. Experiments have shown that we can learn such a mapping that made it possible later to generate relevant descriptions of images or learn the meaning of words without any linguistic resource. ispartof: pages:1764-1765 ispartof: Proceedings of the 22nd European Conference on Artificial Intelligence vol:285 pages:1764-1765 ispartof: European Conference on Artificial Intelligence location:The Hague, The Netherlands date:30 Aug - 3 Sep 2016 status: published

Published
2016

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