<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lehis, Saida</style></author><author><style face="normal" font="default" size="100%">Siam, Abderrahim</style></author><author><style face="normal" font="default" size="100%">Hamouma Moumen</style></author><author><style face="normal" font="default" size="100%">Chergui, Wahid</style></author><author><style face="normal" font="default" size="100%">Souidi, Mohammed-El Habib</style></author><author><style face="normal" font="default" size="100%">Bekhouche, Abdelaali</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multi-Head DDPG for Pursuit-Evasion with Interpretable Behavioral Decomposition</style></title><secondary-title><style face="normal" font="default" size="100%">Ingénierie des Systèmes d’Information</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2025</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">  https://doi.org/10.18280/isi.301204</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">30</style></volume><pages><style face="normal" font="default" size="100%">3117-3130</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	Designing scalable and interpretable control strategies for decentralized multi-agent systems remains a challenge in reinforcement learning (RL). This challenge is particularly evident in pursuit–evasion tasks, which require coordination under partial observability, without explicit communication or centralized guidance. Although deep RL methods achieve strong performance, they typically operate as black boxes, limiting trust and deployment in safety-critical domains. We propose a Multi-Head DDPG architecture that decomposes control into three interpretable force components - pursuit, cohesion, and separation - weighted adaptively to generate context-aware actions. This design enables emergent role differentiation and interpretable self-organization in the model. In grid-based pursuit–evasion benchmarks, our method outperforms DQN, PPO, and standard DDPG in terms of success rate, convergence speed, and generalization, while also yielding transparent collective behaviors. Overall, the results show that weighted force-based behavioral decomposition provides a principled pathway toward achieving both high-performance and explainable multi-agent control.
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</style></abstract><issue><style face="normal" font="default" size="100%">12</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Haouassi, Hichem</style></author><author><style face="normal" font="default" size="100%">Mahdaoui, Rafik</style></author><author><style face="normal" font="default" size="100%">Chouhal, Ouahiba</style></author><author><style face="normal" font="default" size="100%">Bekhouche, Abdelaali</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An efficient classification rule generation for coronary artery disease diagnosis using a novel discrete equilibrium optimizer algorithm</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Intelligent &amp; Fuzzy Systems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs213257</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">43</style></volume><pages><style face="normal" font="default" size="100%">2315-2331</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Many machine learning-based methods have been widely applied to Coronary Artery Disease (CAD) and are achieving high accuracy. However, they are black-box methods that are unable to explain the reasons behind the diagnosis. The trade-off between accuracy and interpretability of diagnosis models is&amp;nbsp;important, especially for human disease. This work aims to propose an approach for generating rule-based models for CAD diagnosis. The classification rule generation is modeled as combinatorial optimization problem and it can be solved by means of metaheuristic algorithms. Swarm intelligence algorithms like Equilibrium Optimizer Algorithm (EOA) have demonstrated great performance in solving different optimization problems. Our present study comes up with a Novel Discrete Equilibrium Optimizer Algorithm (NDEOA) for the classification rule generation from training CAD dataset. The proposed NDEOA is a discrete version of EOA, which use a discrete encoding of a particle for representing a classification rule; new discrete operators are also defined for the particle’s position update equation to adapt real operators to discrete space. To evaluate the proposed approach, the real world Z-Alizadeh Sani dataset has been employed. The proposed approach generate a diagnosis model composed of 17 rules, among them, five rules for the class “Normal” and 12 rules for the class “CAD”. In comparison to nine black-box and eight white-box state-of-the-art approaches, the results show that the generated diagnosis model by the proposed approach is more accurate and more interpretable than all white-box models and are competitive to the black-box models. It achieved an overall accuracy, sensitivity and specificity of 93.54%, 80% and 100% respectively; which show that, the proposed approach can be successfully utilized to generate efficient rule-based CAD diagnosis models.</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue></record></records></xml>