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2014Hanen Lejmi, Lamjed Ben Said,
Agent Decision-Making under Uncertainty: Towards a New E-BDI Agent Architecture Based on Immediate and Expected Emotions
Agent Decision-Making under Uncertainty: Towards a New E-BDI Agent Architecture Based on Immediate and Expected Emotions, 2014
Abstract
Over the last decade, emotions have received considerable attention among scholars in agent oriented systems. In fact a large amount of computational models of emotions has been developed and a new generation of artificial agents has emerged to give rise to emotional agents, in particular the Emotional BDI (EBDI) agents. However, in spite of the several interesting studies that have been conducted to underline the role of emotions in decision-making, few works in the agent community have shed the light on the influences of both immediate and expected emotions to drive decision-making. In this context, we intend to propose a new conceptual model of EBDI agency that involves the interplay among immediate emotions, expected emotions and rational decisions of artificial agents.
Kalthoum Rezgui, Hédia Sellemi,An Ontology-based Profile for Learner Representation in Learning Networks
-, 2014
Abstract
In the context of lifelong learning, learning networks are emerged as alternative and feasible integrated models that merge pedagogical, organizational, and technological perspectives to support and promote the provision of lifelong learning opportunities. Among the significant issues that arise when setting up a learning network is the question of how to support communication between repositories that employ different schemes for describing learner profiles. To guarantee correct interpretation, a semantic common metadata schema is required. This paper aims to propose an ontological structure for representing a learner profile that augments it with semantics and provides a common vocabulary for the exchange of the different learner’s characteristics that can be presented in a learner model. The proposed structure is based on different learner information with respect to well-known learner model specifications. Besides, it reuses terms from well-developed Semantic Web vocabularies which make it semantic web compliant and integrates different domain taxonomies and subject taxonomies that are used as ranges for particular concepts’ slots.
Kalthoum Rezgui, Hédia Sellemi,Extending Moodle Functionalities with Ontology-based Competency Management
-, 2014
Abstract
The Learning Management System (LMS) Moodle is currently the most popular software solution which provides many modules for various teaching and learning purposes. However, several aspects relevant for competency management are typically missing in Moodle. This paper proposes an ontology-based competency management application which is developed as a Moodle extension for supporting the development and assessment of competencies inside a course. Details about the competency ontology adopted for designing the competency-based course structure as well as the competency management features embedded into Moodle are presented. By incorporating these features into a LMS, it becomes possible to manage target competencies together with their associated evidence items and assess proficiency levels reached by students for each target competency. In addition, it becomes possible to generate different types of competency reports depending on the target role (teachers, students or administrators).
Kalthoum Rezgui, Hédia Sellemi,An Ontology-Based Approach to Competency Modeling and Management in Learning Networks
-, 2014
Abstract
With the emergence of the paradigm of Lifelong Learning and the proliferation of the terms « knowledge society », « citizen mobility », or « globalization », competency-based learning and training has known a growing interest in technology-enhanced learning as it provides important benefits for both individuals and organizations by supporting the transformation of learning outcomes into permanent and valuable knowledge assets. In this context, to promote the acquisition and continuous development of new competencies, learning networks have emerged that enables to support the provision of various lifelong learning opportunities. However, lifelong competency development still faces numerous challenges and research issues that need to be addressed, primarily the lack of consensus about a common representation of competencies and competency profiles. This paper analyzes different approaches reported in literature for competency modeling and proposes a competency ontology to formally describe competency-related characteristics of actors and learning resources in learning networks. The proposed ontology also aims to model aspects related to competency information management and tracking in order to support lifelong competency development in learning networks.
Islem Henane, , ,Towards a generic approach for multi-level modeling of renewable resources management systems
Proceedings of the 2014 International Conference on Autonomous Agents and Multi-Agent Systems, 1471–1472. Presented at the Paris, France. Richland, SC: International Foundation for Autonomous Agents and Multiagent Systems., 2014
Abstract
Multi-agent systems are widely used in renewable and natural resources management. Multi-agent systems are able to manage the complexity of such systems characterized by a large number of interacting entities with different levels of granularity and including dynamics of different contexts (ecological, economic, social). In this work, we propose a generic multi-level architecture for renewable and natural resources management.
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2013, Wassim Ayadi, , ,
Survey on Biclustering of Gene Expression Data
Biological Knowledge Discovery Handbook: Preprocessing, Mining, and Postprocessing of Biological Data, 2013
Abstract
Microarrays allow measuring the expression level of a large number of genes under different experimental samples or environmental conditions. The data generated from them are called gene expression data. Gene expression data are usually represented by a matrix M, where the ith row represents the ith gene, the jth column represents the jth condition, and the cell mij represents the expression level of the th gene under the jth condition. In this chapter, the authors make a survey on biclustering of gene expression data. First, the chapter presents the different types of biclusters and groups of biclusters. Then, it discusses the evaluation functions and systematic and stochastic biclustering algorithms. Finally, the chapter focuses on bicluster validation that can qualitatively evaluate the capacity of an algorithm to extract meaningful biclusters from a biological point of view.
, Lamjed Ben SaidA DISTRIBUTED PRIVACY-PRESERVING MODEL FOR E-SERVICES
International Conference on Internet Technologies & Society., 2013
Abstract
In this paper, we propose a model for privacy protection of users in the context of e-services. A system based on our model has to respect a set of properties to preserve the user privacy. These properties are formulated as a set of privacy constraints: the anonymity, the pseudonymity, the unobservability and the unlinkability constraints. To satisfy these constraints we use the Distributed Constraint Satisfaction approach such that: (1) the variables correspond to the user’s credentials, (2) the agents correspond to the set of e-services entities that control these variables and (3) the constraints correspond to the set of privacy constraints. A solution to the problem is achieved when all the privacy constraints are satisfied. To validate the applicability of our proposed model, a set of experimentation results are discussed.
, , , Lamjed Ben SaidMulti-Agent System Model for Container Management Simulation.
In : ICEIS (1). 2013. p. 498-505., 2013
Abstract
This paper discusses an approach to build a multiagent system for simulating container management in a hub port logistics. The simulator has as goal to help assessing and defining container management strategies. This allows to plan and to control the management of containers while minimizing the waiting time and the parasite shifts and insuring the consistency of the performed tasks sequence. The proposed model involves the multipoint of view and the emergence of behavior specific to the theory of complex systems. The paper is structured as follows: first we present related works, then we expose the multiagent model of the simulator, after that we present the internal structure of the agents and finally we provide and discuss first implementation and results.
, Lamjed Ben Said,Multiobjective Analysis of the Multi-Location Newsvendor and Transshipment Models
International Journal of Information Systems and Supply Chain Management (IJISSCM), 6(4), 42-60., 2013
Abstract
Unlike the Newsvendor model, a system based on lateral transshipments allows the unsold inventories to be moved from locations with surplus inventory to fulfill more unmet demands at stocked out locations. Both models were thoroughly studied and researches were usually confined to cost minimization or profit maximization. In this paper, the authors proposed a more realistic multiobjective study of both multi-location Transshipment and Newsvendor inventory models. The aggregate cost, the fill rate, and the shared inventory quantity are formulated as conflicting objectives and solved using two reference multiobjective evolutionary algorithms (SPEA2 and NSGA-II). The proposed models take into account the presence of storage capacity constraints. The obtained Pareto fronts revealed interesting information. When transshipments are allowed, both low aggregate cost and high fill rate levels are ensured. The required shared inventory may have an important variability. The considered objective functions are conflicting and very sensitive to local storage capacities.
, Moez Hammami,Ensemble classifiers for drift detection and monitoring in dynamical Environments
Annual Conference of the Prognostics and Health Management Society 2013, 2013
Abstract
Detecting and monitoring changes during the learning process are important areas of research in many industrial applications. The challenging issue is how to diagnose and analyze these changes so that the accuracy of the learning model can be preserved. Recently, ensemble classifiers have achieved good results when dealing with concept drifts. This paper presents two ensembles learning algorithms BagEDIST and BoostEDIST, which respectively combine the Online Bagging and the Online Boosting with the drift detection method EDIST. EDIST is a new drift detection method which monitors the distance between two consecutive errors of classification. The idea behind this combination is to develop an ensemble learning algorithm which explicitly handles concept drifts by providing useful descriptions about location, speed and severity of drifts. Moreover, this paper presents a new drift diversity measure in order to study the diversity of base classifiers and see how they cope with concept drifts. From various experiments, this new measure has provided a clearer vision about the ensemble’s behavior when dealing with concept drifts.
