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35 results on '"Rejeb, Lilia"'

5. ATiPreTA: AN Analytical Model for Time–Dependent Prediction of Terrorist Attacks

Author

Kebir Oussama, Nouaouri Issam, Rejeb Lilia, and Ben Said Lamjed

Subjects
terrorist attacks, attack classification, mathematical modeling, dynamic behavior simulation, damage prediction, Mathematics, QA1-939, Electronic computers. Computer science, QA75.5-76.95
Abstract

In counter-terrorism actions, commanders are confronted with difficult and important challenges. Their decision-making processes follow military instructions and must consider the humanitarian aspect of the mission. In this paper, we aim to respond to the question: What would the casualties be if governmental forces reacted in a given way with given resources? Within a similar context, decision-support systems are required due to the variety and complexity of modern attacks as well as the enormous quantity of information that must be treated in real time. The majority of mathematical models are not suitable for real-time events. Therefore, we propose an analytical model for a time-dependent prediction of terrorist attacks (ATiPreTA). The output of our model is consistent with casualty data from two important terrorist events known in Tunisia: Bardo and Sousse attacks. The sensitivity and experimental analyses show that the results are significant. Some operational insights are also discussed.

Published
2022
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6. Belief eXtended Classifier System: A New Approach for Dealing with Uncertainty in Sleep Stages Classification

Author

Ferjani, Rahma, Rejeb, Lilia, Said, Lamjed Ben, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Abraham, Ajith, editor, Hanne, Thomas, editor, Castillo, Oscar, editor, Gandhi, Niketa, editor, Nogueira Rios, Tatiane, editor, and Hong, Tzung-Pei, editor

Published
2021
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7. HoneyBees Mating Optimization Algorithm for the Static Bike Rebalancing Problem

Author

Sebai, Mariem, Fatnassi, Ezzeddine, Rejeb, Lilia, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Abraham, Ajith, editor, Siarry, Patrick, editor, Ma, Kun, editor, and Kaklauskas, Arturas, editor

Published
2021
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8. Unsupervised Sleep Stages Classification Based on Physiological Signals

Author

Ferjani, Rahma, Rejeb, Lilia, Said, Lamjed Ben, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Demazeau, Yves, editor, Holvoet, Tom, editor, Corchado, Juan M., editor, and Costantini, Stefania, editor

Published
2020
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12. Evidential Supervised Classifier System: A New Learning Classifier System Dealing with Imperfect Information.

Author

Ferjani, Rahma, Rejeb, Lilia, Abdelkarim, Chedi, and Said, Lamjed Ben

Abstract

Learning Classifier Systems (LCSs) are a kind of evolutionary machine learning algorithms that provide highly adaptive components to deal with real world problems. They have been widely used in resolving complex problems such as decision making and classification. LCSs are flexible algorithms that are able to construct, incrementally, a set of rules and evolve them through the Evolutionary Algorithm (EA). Despite their efficiency, LCSs are not capable of handling imperfect information, which may lead to reduced performance in terms of classification accuracy. We propose a new accuracy-based Michigan-style LCS that integrates the belief function theory in the supervised classifier system. The belief function or evidence theory represents an efficient framework for treating imperfect information. The new approach shows promising results in real world classification problems. [ABSTRACT FROM AUTHOR]

Published
2024
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14. An Adaptive Approach for the Exploration-Exploitation Dilemma and Its Application to Economic Systems

Author

Rejeb, Lilia, Guessoum, Zahia, M’Hallah, Rym, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Dough, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Tuyls, Karl, editor, Hoen, Pieter Jan’t, editor, Verbeeck, Katja, editor, and Sen, Sandip, editor

Published
2006
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15. Firms Adaptation in Dynamic Economic Systems

Author

Rejeb, Lilia, Guessoum, Zahia, Beckmann, M., editor, Künzi, H. P., editor, Fandel, G., editor, Trockel, W., editor, Basile, A., editor, Drexl, A., editor, Dawid, H., editor, Inderfurth, K., editor, Kürsten, W., editor, Schittko, U., editor, Mathieu, Philippe, editor, Beaufils, Bruno, editor, and Brandouy, Olivier, editor

Published
2006
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16. An Adaptive Approach for the Exploration-Exploitation Dilemma for Learning Agents

Author

Rejeb, Lilia, Guessoum, Zahia, M’Hallah, Rym, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Pěchouček, Michael, editor, Petta, Paolo, editor, and Varga, László Zsolt, editor

Published
2005
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22. Tailoring project management practices for decision making: an in-depth comparative study

Author

Zaouga, Wiem, Rejeb, Lilia, and Rabai, Latifa Ben Arfa

Abstract

The efforts to successfully complete projects lead to the development of various project management (PM) standards, best practices and guidelines, issued by different organisational bodies. These PM practices, when appropriately implemented, lead to a better project performance. However, studies on how to adopt and adapt such practices according to management needs, remain limited. In this paper, we are going to focus on how to map the project requirements with the suitable PM practices to support the PM decision making. To respond this question, we put forward an in-depth comparative study amongst the well-established PM practices considering a set of features to pick out their challenges, limits as well as their applicability. Through this comparison, we extend the discussion of PM practices features by contrasting them to three distinct categories of requirements which are technical, contextual and behavioural. This analysis allows us to map each category to its corresponding practice(s). Our finding provides comprehensive recommendation guidelines to both practitioners and researchers in order to improve their decision making in line with the project environment.

Published
2023
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30. The Exploration-Exploitation Dilemma for Adaptive Agents

Author

Rejeb, Lilia, Guessoum, Zahia, Systèmes Multi-Agents (SMA), Laboratoire d'Informatique de Paris 6 (LIP6), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), and Publications, Lip6

Subjects
[INFO]Computer Science [cs], [INFO] Computer Science [cs], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2005

31. Using adaptive Multi-Agent Systems to Simulate Economic Models

Author

Guessoum, Zahia, Rejeb, Lilia, Durand, Rodolphe, Systèmes Multi-Agents (SMA), Laboratoire d'Informatique de Paris 6 (LIP6), and Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)

Subjects
ComputingMilieux_THECOMPUTINGPROFESSION, [INFO]Computer Science [cs]
Abstract

International audience; Economic markets are complex systems. They are characterized by a large and dynamic population of firms. To deal with this complexity, we propose an adaptive multiagent system which models a set of firms in competition with each other within a shared market. The firms are represented by agents; each firm is represented by an adaptive agent. We show the advantages of adaptive agents to represent firms. Moreover, we underline the limits of the economic models which account for the firms only and ignore the organizational forms. We propose a new adaptive multiagent model that includes the organizational forms into the economic models. We simulate this model and discuss its advantages.

Published
2004
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32. Using XCS to build adaptive agents

Author

Guessoum, Zahia, Rejeb, Lilia, Sigaud, Olivier, Systèmes Multi-Agents (SMA), Laboratoire d'Informatique de Paris 6 (LIP6), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Animatlab, and Publications, Lip6

Subjects
[INFO]Computer Science [cs], [INFO] Computer Science [cs], ComputingMethodologies_ARTIFICIALINTELLIGENCE
Abstract

International audience; To deal with dynamic changes of their environment, agents need an adaptive mechanism. This paper proposes an integration of classifier-based framework (named XCS) and an agent-based framework (named DIMA). The result of this integration is an adaptive- agent framework. It has been applied to simulate economic models.

Published
2004

33. An Adaptive Approach for the Exploration-Exploitation Dilemma and Its Application to Economic Systems.

Author

Tuyls, Karl, 't Hoen, Pieter Jan, Verbeeck, Katja, Sen, Sandip, Rejeb, Lilia, Guessoum, Zahia, and M'Hallah, Rym

Abstract

Learning agents have to deal with the exploration-exploitation dilemma. The choice between exploration and exploitation is very difficult in dynamic systems; in particular in large scale ones such as economic systems. Recent research shows that there is neither an optimal nor a unique solution for this problem. In this paper, we propose an adaptive approach based on meta-rules to adapt the choice between exploration and exploitation. This new adaptive approach relies on the variations of the performance of the agents. To validate the approach, we apply it to economic systems and compare it to two adaptive methods originally proposed by Wilson: one local and one global. Moreover, we compare different exploration strategies and focus on their influence on the performance of the agents. [ABSTRACT FROM AUTHOR]

Published
2006
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34. An Adaptive Approach for the Exploration-Exploitation Dilemma for Learning Agents.

Author

Pěchouček, Michael, Petta, Paolo, Varga, László Zsolt, Rejeb, Lilia, Guessoum, Zahia, and M'Hallah, Rym

Abstract

Learning agents have to deal with the exploration-exploitation dilemma. The choice between exploration and exploitation is very difficult in dynamic systems; in particular in large scale ones such as economic systems. Recent research shows that there is neither an optimal nor a unique solution for this problem. In this paper, we propose an adaptive approach based on meta-rules to adapt the choice between exploration and exploitation. This new adaptive approach relies on the variations of the performance of the agents. To validate the approach, we apply it to economic systems and compare it to two adaptive methods: one local and one global. Herein, we adapt these two methods, which were originally proposed by Wilson, to economic systems. Moreover, we compare different exploration strategies and focus on their influence on the performance of the agents. [ABSTRACT FROM AUTHOR]

Published
2005
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35. Emergence de zones conflits dans deux modèles de généralisation cartographique multi-agents

Author

Cécile Duchêne, Guillaume Touya, Laboratoire des Sciences et Technologies de l'Information Géographique (LaSTIG), École nationale des sciences géographiques (ENSG), Institut National de l'Information Géographique et Forestière [IGN] (IGN)-Institut National de l'Information Géographique et Forestière [IGN] (IGN), Occello, Michel, Rejeb, Lilia, and Touya, Guillaume

Subjects
cartographic generalisation, généralisation cartographique, observation, dynamic entity creation, résolution de problème spatial, spatial problem resolution, [INFO]Computer Science [cs], multi-niveau, [INFO] Computer Science [cs], création dynamique d'entités, multi-level, Généralisation
Abstract

We use an agent modeling to handle a complex spatialised problem close to a constraint satisfaction problem: generalization of vector geographic data (aiming at decreasing the level of detail in a geographic dataset). This problem requires to handle agents at different abstraction levels, e.g. a building, a group of buildings , a city. Two methods are proposed that enable to dynamically change the level of abstraction of handled agents, by observing at lowest abstraction level the unsatisfied constraints , and dynamically creating intermediate entities representing groups of agents that do not manage to satisfy their constraints. The first method supposes that the running system is known and can be observed from inside (messages exchanged by agents or remaining actions to try can be checked), the second one supposes that it is seen as a black-box. First elements of comparison are then given., Nous utilisons une modélisation agent pour résoudre un problème spatialisé complexe apparenté à un problème de satisfaction de contraintes : la généralisation de données géographiques vectorielles (diminution du niveau de détail dans un jeu de données géo-graphiques). Ce problème nécessite de mani-puler des agents ayant des niveaux d'abstraction différents, par exemple un bâti-ment, un groupe de bâtiments, une ville. Nous proposons deux méthodes permettant de chan-ger dynamiquement le niveau d'abstraction des entités manipulées, par observation au niveau d'abstraction le plus bas des con-traintes restant non satisfaites, et création dynamique d'entités intermédiaires représen-tant des groupes d'agents proches ne parve-nant pas à satisfaire leurs contraintes. La première méthode suppose que le système en cours d'exécution est connu et peut être obser-vé de l'intérieur (accès aux messages échangés entre agents, aux actions pouvant encore être essayées), la seconde qu'il est vu comme une boîte noire. Des premiers éléments de comparaison sont ensuite donnés. e.

Published
2010

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