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University of Paris 8, France

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19 Papers

VICA: A Vicarious Cognitive Architecture Environment Model for Navigation Among Movable Obstacles

Topics: Multi-Agent Systems; Cognitive Systems; Autonomous Systems; Cognitive Robotics

Authors: Halim Djerroud and Arab Ali-Chérif

Affiliation: Laboratoire Paragraphe, Université Paris VIII, 2 rue de la Liberté 93526 Saint-Denis, France

Digital Object Identifier: 10.5220/0010269602980305

Proceedings of ICAART 2021, ISBN: 978-989-758-484-8

Quick Abstract

This article presents a new Cognitive Architecture Environment model for Navigation Among Movable Obstacles (NAMO). This model is the result of a novel approach based on the Theory of Mind and more particularly on the notion of ’vicariance’ as an essential strategy of the robot’s interaction with outside world. The implementation of our model follows the advances in AI and the Cognitive Robotics research area, where a cognitive architecture environment is represented as a Multi-Agent System (MAS). The MAS representation offers the robot the ability to produce a representation of its environment as well as the possibility to run all types of action simulations in order to anticipate the environment’s reactions. The environment state values, both predictive and real as transcribed during simulation and real action movements, are compared to each other in order to keep the correct ones and avoid errors. This is a continuous learning and leads to the construction of a safe path of actions into a dynamic environment. The experiment results show the efficiency of our model, offering an intelligent guide to the robot in order to perform tasks among mobile agents, by avoiding a maximum number of obstacles.

Environment Engine for Situated MAS

Topics: Agent Models and Architectures; Robot and Multi-Robot Systems

Authors: Halim Djerroud and Arab Cherif

Affiliation: LIASD, Advanced Computing Laboratory of Saint-Denis (LIASD), Paris 8 University, 2 Rue de la Liberté, 93526 Saint-Denis and France

Digital Object Identifier: 10.5220/0007310501290137

Proceedings of ICAART 2019, ISBN: 978-989-758-350-6

Quick Abstract

In the multi-agent system research community, there is general consensus that the environment is important for multi-agent systems (MAS). However, most researchers minimize the responsibilities of the environment by reducing it to inter-agent communication, or neglecting to integrate it as a main element of their MAS models, which can be sufficient depending on the focus and objectives of their work. As a consequence of these decisions, the potential of MAS is not fully exploited. In some cases, the environment is a key element that cannot be written off as inter-agent communication, as it currently is in classical MAS. In our opinion it is important to focus the MAS around the environment. Reducing the environment to inter-agent communication deprives multi-agent systems of great potential. Our point of view is that the environment is an active entity with its own processes that can change its state, regardless of the activity of its embedded agents. We propose including the environment as an entity with a set of laws. Laws can be considered rules that cannot be broken by agents. However, some researchers have been interested in integrating the environment as first class and have proposed some interesting models. Unfortunately, they are not sustained by practical applications. The aim of this paper is to contribute in two ways: first, to propose an MAS model where an environmental engine is integrated; the capabilities of this model are comparable to those of a physics engine. Second, we propose an implementation of this model and some practical cases where it can bring concrete added value.

Related Ontology Subjects/Areas/Topics (4)

Agent Models and Architectures		Artificial Intelligence		Robot and Multi-Robot Systems
Agents

Identify Theft Detection on e-Banking Account Opening

Topics: Learning Paradigms and Algorithms; Applications: Image Processing and Artificial Vision, Pattern Recognition, Decision Making, Industrial and Real World Applications, Financial Applications, Neural Prostheses and Medical Applications, Neural Based Data Mining and Complex Information Process

Authors: Roxane Desrousseaux , Gilles Bernard and Jean-Jacques Mariage

Affiliation: LIASD Laboratory, Paris 8 University and France

Digital Object Identifier: 10.5220/0008648605560563

Proceedings of NCTA 2019/IJCCI, ISBN: 978-989-758-384-1

Quick Abstract

Banks are compelled by financial regulatory authorities to demonstrate whole-hearted commitment to finding ways of preventing suspicious activities. Can AI help monitor user behavior in order to detect fraudulent activity such as identity theft? In this paper, we propose a Machine Learning (ML) based fraud detection framework to capture fraudulent behavior patterns and we experiment on a real-world dataset of a major European bank. We gathered recent state-of-the-art techniques for identifying banking fraud using ML algorithms and tested them on an abnormal behavior detection use case.

Related Ontology Subjects/Areas/Topics (17)

Artificial Intelligence	Learning Paradigms and Algorithms	Physiological Computing Systems
Biomedical Engineering	Methodologies and Methods	Sensor Networks
Biomedical Signal Processing	Neural Networks	Signal Processing
Computational Intelligence	Neurocomputing	Soft Computing
Health Engineering and Technology Applications	Neurotechnology, Electronics and Informatics	Theory and Methods
Human-Computer Interaction	Pattern Recognition

Towards the Enrichment of Arabic WordNet with Big Corpora

Topics: Learning Paradigms and Algorithms; Self-Organization and Emergence; Applications: Image Processing and Artificial Vision, Pattern Recognition, Decision Making, Industrial and Real World applications, Financial Applications, Neural Prostheses and Medical Applications, Neural based Data Mining and Complex Information Processing, Neural Network Software and Applications, Applications of Deep Neural networks, Robotics and Control Applications

Authors: Georges Lebboss ¹ , Gilles Bernard ¹ , Noureddine Aliane ¹ and Mohammad Hajjar ²

Affiliations: ¹ LIASD and Paris 8 University, France , ² Lebanese University and IUT, Lebanon

Digital Object Identifier: 10.5220/0006505701010109

Proceedings of IJCCI 2017, ISBN: 978-989-758-274-5

Quick Abstract

This paper presents a method aiming to enrich Arabic WordNet with semantic clusters extracted from a large general corpus. As the Arabic language is poor in open digital linguistic resources, we built such a corpus (more than 7.5 billion words) with ad-hoc tools. We then applied GraPaVec, a new method for word vectorization using automatically generated frequency patterns, as well as state-of-the-art Word2Vec and Glove methods. Word vectors were fed to a Self Organizing Map neural network model; the clusterings produced were then compared for evaluation with Arabic WordNet existing synsets (sets of synonymous words). The evaluation yields a F-score of 82.1 % for GrapaVec, 55.1 % for Word2Vec's Skipgram, 52.2 % for CBOW and 56.6 % for Glove, which at least shows the interest of the context that GraPaVec takes into account. We end up by discussing parameters and possible biases.

Related Ontology Subjects/Areas/Topics (18)

Artificial Intelligence	Learning Paradigms and Algorithms	Physiological Computing Systems
Biomedical Engineering	Methodologies and Methods	Self-Organization and Emergence
Biomedical Signal Processing	Neural Networks	Sensor Networks
Computational Intelligence	Neurocomputing	Signal Processing
Health Engineering and Technology Applications	Neurotechnology, Electronics and Informatics	Soft Computing
Human-Computer Interaction	Pattern Recognition	Theory and Methods

A Relevant Visual Feature Selection Approach for Image Retrieval

Topics: Features Extraction; Content-Based Indexing, Search, and Retrieval

Authors: Olfa Allani ¹ , Nedra Mellouli ² , Hajer Baazaoui Zghal ³ , Herman Akdag ⁴ and Henda Ben Ghzala ³

Affiliations: ¹ Université Paris 8 and LABORATOIRE RIADI, France , ² Universite Paris 8 Saint-Denis, France , ³ LABORATOIRE RIADI, Tunisia , ⁴ Universite Paris 8, France

Digital Object Identifier: 10.5220/0005306303770384

Proceedings of VISAPP 2015/VISIGRAPP, ISBN: 978-989-758-090-1

Quick Abstract

Content-Based Image Retrieval approaches have been marked by the semantic gap (inconsistency) between the perception of the user and the visual description of the image. This inconsistency is often linked to the use of predefined visual features randomly selected and applied whatever the application domain. In this paper we propose an approach that adapts the selection of visual features to semantic content ensuring the coherence between them. We first design visual and semantic descriptive ontologies. These ontologies are then explored by association rules aiming to link semantic descriptor (a concept) to a set of visual features. The obtained feature collections are selected according to the annotated query images. Different strategies have been experimented and their results have shown an improvement of the retrieval task based on relevant feature selections.

Related Ontology Subjects/Areas/Topics (3)

Computer Vision, Visualization and Computer Graphics

Features Extraction

Image and Video Analysis

An Experimentation Line for Underlying Graphemic Properties - Acquiring Knowledge from Text Data with Self Organizing Maps

Authors: Gilles Bernard , Nourredine Aliane and Otman Manad

Affiliation: PARIS 8 University, France

Digital Object Identifier: 10.5220/0005577706590666

Proceedings of ANNIIP 2015/ICINCO, ISBN: 978-989-758-122-9

Quick Abstract

We present an experimentation line that encompasses various stages for research on graphemes distribution and unsupervised classification. We aim to help close the gap between recent research results showing the abilities of unsupervised learning and clustering algorithms to detect underlying properties of phonemes and the present possibilities of Unicode textual representation. Our procedures need to ensure repeatability and guarantee that no information is implicitely present in the preprocessing of data. Our approach is able to categorize potential graphemes correctly, thus showing that not only phonemic properties are indeed present in textual data, but that they can be automatically retrieved from raw-unicode text data and translated into phonemic representations. By the way, we observe that SOM algorithm copes well with very sparse vectors.

Appearance-based Eye Control System by Manifold Learning

Topics: Machine Learning Technologies for Vision; Human and Computer Interaction

Authors: Ke Liang ¹ , Youssef Chahir ¹ , Michèle Molina ² , Charles Tijus ¹ and François Jouen ¹

Affiliations: ¹ EPHE Paris, France , ² University of Caen, France

Digital Object Identifier: 10.5220/0004682601480155

Proceedings of VISAPP 2014/VISIGRAPP, ISBN: 978-989-758-009-3

Quick Abstract

Eye-movements are increasingly employed to study usability issues in HCI (Human-Computer Interacetion) contexts. In this paper we introduce our appearance-based eye control system which utilizes 5 specific eye movements, such as closed-eye movement and eye movements with gaze fixation at the positions (up, down, right, left) for HCI applications. In order to measure these eye movements, we employ a fast appeance-based gaze tracking method with manifold learning technique. First we propose to concatenate local eye appearance Center-Symmetric Local Binary Pattern(CS-LBP) descriptor for each subregion of eye image to form an eye appearance feature vector. The calibration phase is then introduced to construct a trainning samples by spectral clustering. After that, Laplacian Eigenmaps will be applied to the trainning set and unseen input together to get the structure of eye manifolds. Finally we can infer the eye movement of the new input by its distances with the clusters in the trainning set. Experimental results demonstrate that our system with quick 4-points calibration not only can reduce the run-time cost, but also provide another way to mesure eye movements without mesuring gaze coordinates to a HCI application such as our eye control system.

Related Ontology Subjects/Areas/Topics (5)

Applications and Services		Enterprise Information Systems		Human-Computer Interaction
Computer Vision, Visualization and Computer Graphics		Human and Computer Interaction

Towards the Quality Improvement of Web Applications by Neuroscience Techniques

Topics: Requirements Elicitation and Specification; Software Quality; Quality Assurance

Authors: F.J. Domínguez-Mayo ¹ , G. Kubryk ² , M.J. Escalona ¹ , G. Denhière ² , M. Mejías ¹ and C. Tijus ²

Affiliations: ¹ University of Seville, Spain , ² Université Paris 8, France

Digital Object Identifier: 10.5220/0005040303370344

Proceedings of ICSOFT-EA 2014/ICSOFT, ISBN: 978-989-758-036-9

Quick Abstract

User-centered design not only requires designers to analyse and anticipate how users are likely to use a Web application, but also to validate their assumptions with regard to user behaviour in real environments. Cognitive neuroscience, for its part, addresses the questions of how psychological functions are produced by neural circuitry. The emergence of powerful new measurement techniques allows neuroscientists and psychologists to address abstract questions such as how human cognition and emotion are mapped to specific neural substrates. This paper focus on the validation of user-centered designs and requirements of Web applications by neuroscience techniques and suggest the use of these techniques to achieve efficient and effectiveness validated designs by real behavior of potential users.

Related Ontology Subjects/Areas/Topics (7)

Quality Assurance	Software Engineering	Software Project Management
Requirements Elicitation and Specification	Software Engineering Methods and Techniques	Software Quality Management
Service-Oriented Software Engineering and Management

Sampling-based Multi-robot Motion Planning

Authors: Zhi Yan , Nicolas Jouandeau and Arab Ali Cherif

Affiliation: Paris 8 University, France

Digital Object Identifier: 10.5220/0004605005490554

Proceedings of IVC&ITS 2013/ICINCO, ISBN: 978-989-8565-70-9

Quick Abstract

This paper describes a sampling-based approach to multi-robot motion planning. The proposed approach is centralized, which aims to reduce interference between mobile robots such as collision, congestion and deadlock, by increasing the number of waypoints. The implementation based on occupancy grid map is decomposed into three steps: the first step is to identify primary waypoints by using the Voronoi diagram, the second step is to generate additional waypoints by sampling the Voronoi diagram, and the last step is to assign the waypoints to robots by using the Hungarian method. The approach has been implemented and tested in simulation and the experimental results show a good system performance for multi-robot motion planning.

Through a Fuzzy CTL Logic for Modelling Urban Trajectories - A Framework for Modelling City Evolution from Past to Future

Topics: Soft Computing; Knowledge Representation and Reasoning; Uncertainty in AI

Authors: Asma Zoghlami ¹ , Cyril de Runz ² and Herman Akdag ¹

Affiliations: ¹ University of Paris 8, France , ² University of Paris 8 and University of Reims, France

Digital Object Identifier: 10.5220/0004256305140518

Proceedings of ICAART 2013, ISBN: 978-989-8565-39-6

Quick Abstract

A city is by definition a relatively large town of a significant importance. It is a centre of population, commerce, culture, industry, etc. The city evolves over time and gets morphological, sociological, economic and political transformations. Geographic Information System (GIS) may be used in spatial analysis of both the current city and its evolution over time. Based on the past and the present of a city, we are interested in developing a methodology that goes from the spatiotemporal modelling of its evolution to its prediction in the future. The motivation behind this research is to create a tool for the decision support at the disposal of the town hall. This tool aims to help making future decisions about investments, transport networks, infrastructures, etc. In this paper, we propose a framework that allows defining the possible trajectories of the city following the spatial, temporal and functional dimensions. The definition of those trajectories will be attached to a reasoning based on logic according to modalities, time and the imperfect nature of the information (imprecision, uncertainty, etc.).

Related Ontology Subjects/Areas/Topics (10)

Artificial Intelligence	Informatics in Control, Automation and Robotics	Soft Computing
Artificial Intelligence and Decision Support Systems	Intelligent Control Systems and Optimization	Symbolic Systems
Computational Intelligence	Knowledge Representation and Reasoning	Uncertainty in AI
Enterprise Information Systems

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