Browsing by Author "Abderrahim Siam"

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    A Category-theoretic Approach to Organization-based Modeling of Multi Agent Systems on the Basis of Collective Phenomena and Organizations in Human Societies
    (2016-06-30) Abderrahim Siam
    This paper presents an idea of using category theory for developing organizational multi-agent systems by taking inspiration from collective phenomena and organizations in human societies. Category theory is used for studying and formalizing organizations and collective phenomena in human societies with the aim of capturing their logics into categorical models. Afterward, the captured models are mapped categorically to categorical MAS organizational models. This way of thinking allows studying properties of result MAS organizational models as well as properties of organizations in human societies such as stability and adaptation before taking them as landmarks for developing MAS organizational models.
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    A Discrete Crow Search Algorithm for Mining Quantitative Association Rules
    (2021) Abderrahim Siam
    Associationrulesarethespecificdataminingmethodsaimingtodiscoverexplicitrelationsbetweenthe differentattributesinalargedataset.However,inreality,severaldatasetsmaycontainbothnumericand categoricalattributes.Recently,manymeta-heuristicalgorithmsthatmimicthenaturearedeveloped forsolvingcontinuousproblems.Thisarticleproposesanewalgorithm,DCSA-QAR,formining quantitativeassociationrulesbasedoncrowsearchalgorithm(CSA).Toaccomplishthis,newoperators aredefinedtoincreasetheabilitytoexplorethesearchingspaceandensurethetransitionfromthe continuoustothediscreteversionofCSA.Moreover,anewdiscretizationalgorithmisadoptedfor numericalattributestakingintoaccountdependenciesprobablythatexistbetweenattributes.Finally, toevaluatetheperformance,DCSA-QARiscomparedwithparticleswarmoptimizationandmono andmulti-objectiveevolutionaryapproachesforminingassociationrules.Theresultsobtainedover real-worlddatasetsshowtheoutstandingperformanceofDCSA-QARintermsofqualitymeasures.
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    Cours Pour les étudiants de la 3ème année licence Systèmes informatiques Paradigmes de programmation
    (2020) Abderrahim Siam
    le Il est destiné essentiellement aux étudiants de la 3 présent manuscrit est un support de cours de la matière ème année licence informatique. A ce stade de « Paradigmes de programmation ». la formation, les étudiants sont bien familiarisés avec les concepts de base de la programmation dite impérative et celle orientée objets. D’autres paradigmes de programmation sont à découvrir à travers les différents chapitres constituant ce cours. La manière avec laquelle nous avons approché les différents paradigmes de programmation consiste à les situer dans un cadre générale de l’évolution de la programmation depuis le début de son histoire tout en insistant sur les concepts clés de chaque paradigme, ses pragmatiques et pratiques de programmation, ses innovations importantes ainsi que ses limites ouvrant les portes pour l’exploration de nouvelles voies donnant naissances à d’autres paradigmes de programmation
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    Minimal synchrony for implementing Timely Provable Reliable Send primitive with Byzantine failures
    (2023-04-27) Abderrahim Siam
    Broadcast abstractions are among the most important concepts in the field of fault tolerant distributed computing. These abstractions are used by consensus algorithms as a fundamental building block for ensuring that all correct processes in the system decide the same value. The Timely Provable Reliable Send primitive is among these broadcast abstractions with which we guarantee that messages are delivered correctly and in a timely manner, even in the presence of faulty processes. In this paper, we present an authenticated algorithm implementing provable reliable send primitive with very few eventually synchronous links. In other words, this algorithm assumes that there is a ⋄〈t + 1〉-sink in the system. A ⋄〈t + 1〉- sink is a correct process where the number of incoming eventually timely links that connecting it with correct processes is (t + 1) (including itself). We also show that a ⋄〈t + 1〉-sink is the minimal synchrony assumption for implementing this primitive in a Byzantine system where an authentication mechanism is available.
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    MULTI-AGENT DYNAMIC LEADER-FOLLOWER PATH PLANNING APPLIED TO THE MULTI-PURSUER MULTI-EVADER GAME
    (2023) Abderrahim Siam
    Abstract. Multi-agent collaborative path planning focuses on how the agents have to coordinate their displacements in the environment to achieve different targets or to cover a specific zone in a minimum of time. Reinforcement learning is often used to control the agents’ trajectories in the case of static or dynamic targets. In this paper, we propose a multi-agent collaborative path planning based on reinforcement learning and leader-follower principles. The main objectives of this work are the development of an applicable motion planning in a partially observable environment, and also, to improve the agents’ cooperation level during the tasks’ execution via the creation of a dynamic hierarchy in the pursuit groups. This dynamic hierarchy is reflected by the possibility of reattributing the roles of Leaders and Followers at each iteration in the case of mobile agents to decrease the task’s execution time. The proposed approach is applied to the Multi-Pursuer Multi-Evader game in comparison with recently proposed path planning algorithms dealing with the same problem. The simulation results reflect how this approach improves the pursuit capturing time and the payoff acquisition during the pursuit.
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    Multi-agent pursuit coalition formation based on a limited overlapping of the dynamic groups
    (2019) Abderrahim Siam
    Abstract. Coalition formation algorithms based on organizational modeling frameworks can be considered as the earliest approaches applied to the Pursuit-Evasion problem. In this paper we have based on an extension of AGRMF (AgentGroup-Role-Membership-Function) organizational model with a limited overlapping degree of the pursuit groups to allow the coalition of the pursuers. The limited overlapping provides certain equilibrium between the pursuit groups through the elimination of negative externality problem. Regarding the path planning we have based on Markov Decision Process principles (MDP) resolved via the application of value iteration algorithm. To avoid the obstacles encountered, we have based on the recent Reward Bug Algorithm (RBA). This algorithm is based on the payoff returned by the application of MDP. The simulation results reflect the improvement provided by this new approach in relation to recent methods applied to this kind of problems.
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    Multi-Agent Pursuit-Evasion Game Based on Organizational Architecture
    (2019-03-01) Abderrahim Siam
    Multi-agent coordination mechanisms are frequently used in pursuit-evasion games with the aim of enabling the coalitions of the pursuers and unifying their individual skills to deal with the complex tasks encountered. In this paper, we propose a coalition formation algorithm based on organizational principles and applied to the pursuit-evasion problem. In order to allow the alliances of the pursuers in different pursuit groups, we have used the concepts forming an organizational modeling framework known as YAMAM (Yet Another Multi Agent Model). Specifically, we have used the concepts Agent, Role, Task, and Skill, proposed in this model to develop a coalition formation algorithm to allow the optimal task sharing. To control the pursuers' path planning in the environment as well as their internal development during the pursuit, we have used a Reinforcement learning method (Q-learning). Computer simulations reflect the impact of the proposed techniques. ACM CCS (2012) Classification: Computing methodologies → Artificial intelligence → Distributed artificial intelligence → Multi-agent systems Theory of computation → Theory and algorithms for application domains → Algorithmic game theory and mechanism design → Convergence and learning in games
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    Novel energy-aware approach to resource allocation in cloud computing
    (2021-12) Abderrahim Siam
    Abstract. In this paper, we address the issue of resource allocation in a Cloud Computing environment. Since the need for cloud resources has led to the rapid growth of data centers and the waste of idle resources, high-power consumption has emerged. Therefore, we develop an approach that reduces energy consumption. Decision-making for adequate tasks and virtual machines (VMs) with their consolidation minimizes this latter. The aim of the proposed approach is energy efficiency. It consists of two processes; the first one allows the mapping of user tasks to VMs. Whereas, the second process consists of mapping virtual machines to the best location (physical machines). This paper focuses on this latter to develop a model by using a deep neural network and the ELECTRE methods supported by the K-nearest neighbor classifier. The experiments show that our model can produce promising results compared to other works of literature. This model also presents good scalability to improve the learning, allowing, thus, to achieve our objectives.