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34 results on '"Mukesh Barange"'

22. A novel focus encoding scheme for addressee detection in multiparty interaction using machine learning algorithms

Author

Julien Saunier, Usman Malik, Alexandre Pauchet, Mukesh Barange, Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU), Equipe Multi-agent, Interaction, Décision (MIND - LITIS), Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes (LITIS), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Université Le Havre Normandie (ULH), Normandie Université (NU)-Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Normandie Université (NU), Institut des Systèmes Intelligents et de Robotique (ISIR), and Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Scheme (programming language), Computer science, Feature selection, 02 engineering and technology, 010501 environmental sciences, Machine learning, computer.software_genre, 01 natural sciences, Task (project management), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 0202 electrical engineering, electronic engineering, information engineering, Encoding (semiotics), [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, computer.programming_language, Focus (computing), business.industry, Deep learning, Turn-taking, Human-Computer Interaction, [INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA], Signal Processing, Dyadic interaction, 020201 artificial intelligence & image processing, Artificial intelligence, business, computer, Algorithm
Abstract

Addressee detection is a fundamental task for seamless dialogue management and turn taking in human-agent interaction. Though addressee detection is implicit in dyadic interaction, it becomes a challenging task when more than two participants are involved. This article proposes multiple addressee detection models based on smart feature selection and focus encoding schemes. The models are trained using different machine learning and deep learning algorithms. This research work improves existing baseline accuracies for addressee prediction on two datasets. In addition, the article explores the impact of different focus encoding schemes in several addressee detection cases. Finally, an implementation strategy for addressee detection model in real-time is discussed.

Published
2021
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23. A Generic Machine Learning based Approach for Addressee Detection in Multiparty Interaction

Author

Julien Saunier, Naser Ghannad, Mukesh Barange, Alexandre Pauchet, Usman Malik, Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes (LITIS), Université Le Havre Normandie (ULH), Normandie Université (NU)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA), École Nationale d'Ingénieurs de Brest (ENIB), Equipe Multi-agent, Interaction, Décision (MIND - LITIS), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA)-Université Le Havre Normandie (ULH), Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), and Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)

Subjects
Computer science, business.industry, Human-centered computing → Natural language interfaces, Turn-taking, 02 engineering and technology, 010501 environmental sciences, Dialogue management, Machine learning, computer.software_genre, 01 natural sciences, Multimodal interaction, Task (project management), CCS CONCEPTS, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Data set, [INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA], Computing methodologies → Machine learning, Dyadic interaction, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], business, computer, 0105 earth and related environmental sciences
Abstract

International audience; Addressee detection is one of the most fundamental tasks for seamless dialogue management and turn taking in human-agent interaction. Whereas addressee detection is implicit in dyadic interaction, it becomes a challenging task in multiparty interactions when more than two participants are involved. Existing research works employ either rule-based or statistical approaches for addressee detection. However, most of these works either have been tested on a single data set or only support a fixed number of participants. In this article, we propose a model based on generic features to predict the addressee in data sets with varying number of participants. The results tested on two different corpora show that the proposed model outperforms existing baselines.

Published
2019
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24. TERT Promoter Mutation in Adult Glioblastomas: It's Correlation with Other Relevant Molecular Markers

Author

Tejpal Gupta, Ayushi Sahay, Omshree Shetty, Rakesh Jalali, Mukesh Barange, Jayantsastri Goda, Mamta Gurav, Prakash Shetty, Aliasagar Moyiadi, and Sridhar Epari

Subjects
Mutation, medicine.diagnostic_test, business.industry, Wild type, medicine.disease_cause, Telomere, Neurology, medicine, Cancer research, Immunohistochemistry, EGFR Gene Amplification, Telomerase reverse transcriptase, Neurology (clinical), business, neoplasms, ATRX, Fluorescence in situ hybridization
Abstract

Background Telomerase reverse transcriptase promoter (pTERT) mutation is a dominant altered telomere maintenance mechanism in primary glioblastomas (GBMs). Objective The aim of this study was to correlate pTERT mutations with clinico-histological features and other molecular markers (p53 protein-expression, ATRX protein-expression, IDH mutations, EGFR gene amplification and MGMT methylation) in adult GBMs. Materials and methods Evaluated for histological patterns, p53 and ATRX protein expression by immunohistochemistry (IHC), IDH mutations by IHC followed by sequencing in IHC negative cases, EGFR gene amplification by fluorescence in situ hybridization, MGMT promoter methylation by methylation-specific PCR and pTERT mutation by sequencing. Results A total of 155 adult supratentorial GBMs [age-range 20-80 years] formed study cohort. 15.6% were IDH1R132 mutated, none were IDH2R172 mutated and 27% were EGFR amplified. 43% were MGMT methylated and were more common with IDH-mutation (mIDH) than EGFR amplification. 90% of mIDH (but no EGFR amplified) cases showed ATRX-loss. 43.5% were pTERT mutated (C228T was the commonest type) and were mutually exclusive with ATRX-loss. 14% of mIDH and 42% of EGFR amplified cases showed pTERT mutation, the latter was more commonly pMGMT unmethylated (63.6%). Conclusions 43.5% of the GBMs showed pTERT mutation (C228T was commonest; 72%). pTERT mutations were mutually exclusive with ATRX protein loss, more commonly associated with IDH wild type and EGFR amplified GBMs.

Published
2021
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25. Performance Comparison of Machine Learning Models Trained on Manual vs ASR Transcriptions for Dialogue Act Annotation

Author

Mukesh Barange, Usman Malik, Julien Saunier, Alexandre Pauchet, Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes (LITIS), Université Le Havre Normandie (ULH), Normandie Université (NU)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA), Equipe Multi-agent, Interaction, Décision (MIND - LITIS), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA)-Université Le Havre Normandie (ULH), and Saunier, Julien

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], InformationSystems_INFORMATIONINTERFACESANDPRESENTATION(e.g.,HCI), Computer science, 02 engineering and technology, [INFO] Computer Science [cs], Machine learning, computer.software_genre, ComputingMethodologies_ARTIFICIALINTELLIGENCE, Task (project management), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Annotation, Transcription (linguistics), 0202 electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs], ComputingMilieux_MISCELLANEOUS, 060201 languages & linguistics, business.industry, Statistical model, 06 humanities and the arts, Support vector machine, Bag-of-words model, Test set, 0602 languages and literature, Task analysis, 020201 artificial intelligence & image processing, Artificial intelligence, business, computer
Abstract

Automatic dialogue act annotation of speech utterances is an important task in human-agent interaction in order to correctly interpret user utterances. Speech utterances can be transcribed manually or via Automatic Speech Recognizer (ASR). In this article, several Machine Learning models are trained on manual and ASR transcriptions of user utterances, using bag of words and n-grams feature generation approaches, and evaluated on ASR transcribed test set. Results show that models trained using ASR transcriptions perform better than algorithms trained on manual transcription. The impact of irregular distribution of dialogue acts on the accuracy of statistical models is also investigated, and a partial solution to this issue is shown using multimodal information as input.

Published
2018

26. LGG-08. PILOCYTIC ASTROCYTOMAS, EXHIBIT DIFFERENTIAL AGE-BASED PATTERNS OF BRAFV600E AND BRAF GENE FUSIONS ACROSS DIFFERENT LOCATIONS

Author

Aliasgar Moiyadi, Tejpal Gupta, Sridhar Epari, Rakesh Jalali, Mamta Gurav, Rahul Krishnatry, Omshree Shetty, Prakash Shetty, Hetakshi Kurani, Jayant Sastri Goda, Ayushi Sahay, Mukesh Barange, and Girish Chinaswamy

Subjects
Cancer Research, Abstracts, Oncology, Pilocytic Astrocytomas, Cancer research, Neurology (clinical), Biology, Gene, Differential (mathematics)
Abstract

INTRODUCTION: Pilocytic astrocytomas (PCA) are characterised by BRAF fusions and V600E mutation. MATERIAL and METHODS: FFPE tissues of PCA diagnosed during 2011-17 were evaluated for BRAFV600E mutation by Sanger sequencing and KIAA1549:BRAF fusion transcripts (KIAA1549:BRAF 16-9, KIAA1549:BRAF 15-9 and KIAA1549:BRAF 16-11) by reverse transcriptase polymerase chain reaction. RESULTS: 272 cases of PCA of age range 1-46 years (≤14 years:176; 15-25 yrs:76, 26-39yrs:19 and >39yrs:1) formed the sample. BRAFV600E and fusions were mutually exclusive. 25 of 234 (10.7%) were BRAFV600E mutated [cerebellum: 9/90(10%), suprasellar: 5/30(17%), CH: 4/27(15%), brain stem: 2/14(14%) and thalamic: 2/13(15%) 4th ventricle: 1/10 (10%) and 3rd ventricle: 2/7 (28.6%). Whereas 75 of 224 (33.5%) were BRAF fusion [KIAA1549-BRAF 16-9 (n: 52), 15-9 (n:15) & 16-11 (n:8)] positive. 40% of cerebellar (36/91), 37% of suprasellar (10/27), 43% of brain stem (6/14), 36% of spinal (5/14), 21% of CH (5/24), 21% of ON (4/19), 27% of thalamic (3/11), 55.6% of 4th ventricle (5/9) and 50% of pineal (1/2) were fusion positive. None of the 19 ON cases showed BRAFV600E. It was also not detected in 25 yrs age; but was seen in 15.4% (10/65) of cases in 15-25yrs. BRAF fusions were more common in ≤14 yrs (57/145; 39%). Interestingly, none of cases >30 years showed BRAF alterations. Two cases were associated with NF-1 both were negative for BRAF alterations. CONCLUSIONS: BRAF fusions are common than BRAFV600E in PCA across all locations and interestingly both are extremely rare >30years.

Published
2018

27. A methodology for the design of pedagogically adaptable learning environments

Author

Ronan Querrec, Julien Saunier, Bernard Blandin, Mukesh Barange, Buche, Cédric, Centre Européen de Réalité Virtuelle (CERV), and École Nationale d'Ingénieurs de Brest (ENIB)

Subjects
0209 industrial biotechnology, Engineering, 021103 operations research, Multimedia, business.industry, Learning environment, 0211 other engineering and technologies, 02 engineering and technology, [INFO] Computer Science [cs], Virtual reality, UML meta-model, computer.software_genre, Training (civil), Preventive maintenance, Intervention (law), 020901 industrial engineering & automation, pedagogical scenario, learning environment, Digital resources, [INFO]Computer Science [cs], business, computer, Instructional simulation
Abstract

In the last decades, the industry has profoundly integrated the use of digital resources in their production process. However, these assets are rarely re-used for the training of the users, operators and technicians that have to interact with these objects. Furthermore, although training and learning environments are classical applications of virtual reality, the design of these environments is generally ad hoc, i.e. dedicated to specific operations on specific objects, hence requiring the intervention of programmers whenever a modification of the pedagogical scenario is required. In this article, we propose a methodology to design adaptable virtual environments, by separating the role of the different protagonists that play a part in the creation of learning environments. In particular, its goal is to allow the teachers to implement different scenarios according to the level of the trainees and to the pedagogical objectives without the intervention of computer scientists. An example of adaptable wind turbine environment is shown, with three different learning situations: simulator, safety training and preventive maintenance training.

Published
2016
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28. Towards Generic Multimodal Interaction Systems based on machine learning and context awareness

Author

Usman Malik, Mukesh Barange, Julien Saunier, Alexandre Pauchet, Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes (LITIS), Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Université Le Havre Normandie (ULH), Normandie Université (NU), Equipe Multi-agent, Interaction, Décision (MIND - LITIS), Normandie Université (NU)-Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU), and Pauchet, Alexandre

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA], [INFO.INFO-MA] Computer Science [cs]/Multiagent Systems [cs.MA], [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], [INFO.INFO-HC] Computer Science [cs]/Human-Computer Interaction [cs.HC], ComputingMilieux_MISCELLANEOUS, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience

Published
2018

29. Multiparty Interactions for Coordination in a Mixed Human-Agent Teamwork

Author

Alexandre Pauchet, Julien Saunier, Mukesh Barange, Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes (LITIS), Université Le Havre Normandie (ULH), Normandie Université (NU)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA), Equipe Multi-agent, Interaction, Décision (MIND - LITIS), and Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA)-Université Le Havre Normandie (ULH)

Subjects
Teamwork, Computer science, media_common.quotation_subject, Context (language use), 0102 computer and information sciences, 02 engineering and technology, Plan (drawing), computer.software_genre, 01 natural sciences, 010201 computation theory & mathematics, Order (exchange), Virtual machine, Human–computer interaction, 0202 electrical engineering, electronic engineering, information engineering, Human agent, 020201 artificial intelligence & image processing, [INFO]Computer Science [cs], Set (psychology), computer, Human learning, ComputingMilieux_MISCELLANEOUS, media_common
Abstract

Virtual environments for human learning enable one or more users to interact with virtual agents in order to perform their tasks. This collaboration necessitates that the members of the team share a set of beliefs and reason about resources, plans and actions to be implemented. This article introduces a new multiparty coordination model allowing several virtual and human agents to dialogue and reason about the tasks that the user must learn. The proposed model relies on a shared plan based approach to represent the beliefs of the team members. The management of the multiparty aspect makes it possible to differentiate the behaviors to be produced according to the type of receiver of a communication: recipient or listener. Finally, in the context of learning a procedural activity, a study examines the effect of our multiparty model on a learner. Results show that the use of proactive pedagogical agents with multiparty competencies boosts the construction of common beliefs.

Published
2017

30. Designing adaptable virtual reality learning environments

Author

Mukesh Barange, Joanna Taoum, Bernard Blandin, Ronan Querrec, Julien Saunier, Saunier, Julien, Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes (LITIS), Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Université Le Havre Normandie (ULH), Normandie Université (NU), Centre Européen de Réalité Virtuelle (CERV), École Nationale d'Ingénieurs de Brest (ENIB), Lab-STICC_ENIB_CID_IHSEV, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Université Le Havre Normandie (ULH), Normandie Université (NU)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), and Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA)

Subjects
Multimedia, Computer science, 05 social sciences, 050301 education, 020207 software engineering, 02 engineering and technology, Virtual reality, [INFO] Computer Science [cs], computer.software_genre, Preventive maintenance, Intervention (law), 0202 electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs], 0503 education, computer, ComputingMilieux_MISCELLANEOUS, Instructional simulation
Abstract

The EAST (Scientific and technical learning environments) project aims at stimulating the interest of young people for science through virtual reality environments, based on industrial assets. Although training and learning environments are classical applications of virtual reality, the design of these environments is generally ad hoc, hence requiring the intervention of programmers whenever a modification of the pedagogical scenario is required. In this paper, we propose a methodology to design virtual environments which can be adapted by teachers to implement different scenarios according to the level of the trainees and to the pedagogical objectives. Current demonstrators include a windmill with three different learning situations: simulator, safety training and preventive maintenance training.

Published
2016

31. Communicative Capabilities of Agents for the Collaboration in a Human-Agent Team

Author

Mukesh Barange, Kabil, A., Keukelaere, C., Chevaillier, P., École Nationale d'Ingénieurs de Brest (ENIB), Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Lab-STICC_ENIB_CID_IHSEV, Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), AIRIA, ANR-10-CORD-0012,CORVETTE,COllaboRative Virtual Environment Technical Training and Experiment(2010), Chevaillier, Pierre, and CONTENUS ET INTERACTIONS - COllaboRative Virtual Environment Technical Training and Experiment - - CORVETTE2010 - ANR-10-CORD-0012 - CONTINT - VALID

Subjects
Human-Computer Interaction, Cooperation, Decision-Making, Dialogue Management, [INFO]Computer Science [cs], [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], [INFO] Computer Science [cs], [INFO.INFO-HC] Computer Science [cs]/Human-Computer Interaction [cs.HC], virtual humans
Abstract

International audience; The coordination is an essential ingredient for the human-agent teamwork. It requires team members to share knowledge to establish common grounding and mutual awareness among them. In this paper, we propose a behavioral architecture C 2 BDI that allows to enhance the knowledge sharing using natural language communication between team members. We define collaborative conversation protocols that provide proactive behavior to agents for the coordination between team members. We have applied this architecture to a real scenario in a col-laborative virtual environment for training. Our solution enables users to coordinate with other team members.

Published
2014

33. Échange de Connaissances entre Utilisateurs et Agents Autonomes dans les EVFC

Author

Mukesh Barange, Rozenn Bouville Berthelot, Pierre Chevaillier, Camille de Keukelaere, Valérie Gouranton, Alexandre Kabil, Thomas Lopez, Florian Nouviale, ARNALDI Bruno, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), École Nationale d'Ingénieurs de Brest (ENIB), Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA), 3D interaction with virtual environments using body and mind (Hybrid), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-MEDIA ET INTERACTIONS (IRISA-D6), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), ANR-10-CORD-0012,CORVETTE,COllaboRative Virtual Environment Technical Training and Experiment(2010), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES), CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA), Gouranton, Valérie, and CONTENUS ET INTERACTIONS - COllaboRative Virtual Environment Technical Training and Experiment - - CORVETTE2010 - ANR-10-CORD-0012 - CONTINT - VALID

Subjects
[INFO.INFO-GR] Computer Science [cs]/Graphics [cs.GR], [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR]
Abstract

National audience; Cet article propose une approche innovante de la gestion et de l'échange de connaissances entre utilisateurs et agents autonomes dans les Environnements Virtuels de Formation Collaboratifs. Afin de faciliter à la fois le dialogue et la réalisation de tâches collaboratives au sein d'une équipe pouvant être composée d'utilisateurs et d'agents autonomes, nous introduisons une entité commune à ces derniers : le Shell. Cette entité regroupe la gestion des connaissances et le contrôle du mannequin qui lui est attaché. Les agents autonomes, basés sur une architecture nommée C-BDI, exploitent les connaissances du Shell auquel ils sont liés afin d'alimenter leur processus de prise de décision et d'enrichir leur dialogue, basé sur une approche Information-State. Les utilisateurs peuvent également accéder aux connaissances de leur Shell respectif à travers une in- terface utilisateur.

Published
2013

34. Semantic modeling of Virtual Environments using MASCARET

Author

Frédéric Devillers, Mukesh Barange, Pierre Chevaillier, Julien Soler, Thanh-Hai Trinh, Pierre De Loor, and Ronan Querrec

Subjects
Knowledge representation and reasoning, Computer science, Human–computer interaction, Multi-agent system, Semantic computing, Virtual reality, Ontology (information science), Semantic data model, Semantics, Metamodeling
Abstract

Many Virtual Reality (VR) applications, such as Virtual Learning Environments or Interactive Virtual Tours, are based on a rich semantic description of the environment and tasks that users have to perform. These applications are built upon Virtual Environments (VEs) in which artificial agents act autonomously while interacting in realtime with users. Semantic modelling of a VR environment makes it possible the knowledge-driven access from the description of VEs that simplifies the development of VR applications. It eases the development of these types of applications. Semantic modelling should provide a consistent representation of the following aspects: 1) The simulated world, its structure and the behavior of its entities, 2) Interactions and tasks, that users and agents can perform in the environment, 3) Knowledge items, that autonomous agents can use for decision-making or for communication with users. This paper presents MASCARET, a model-based approach, for the design of semantic VR environments. This approach is based on the Unified Modeling Language (UML). In this approach, UML is used to provide a knowledge-driven access to the semantic contents of the VE and not for code generation, as in classical software development process. Interests of a UML-based approach are that its metamodel covers different views of the semantic modelling: ontology, structure, behaviors, interactions, activities. It is also an extensible language that can be specialized to provide formal operational semantics. We also present how MASCARET can be used to develop content-rich interactive applications that can be deployed over various VR platforms. Finally, we discuss the benefits of such a metamodel-based approach and show how the multi-layer semantic model can be used in different VR applications, in which adaptive behaviors of artificial agents acting within complex environments have to be simulated.

Published
2012
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