This paper considers the deployment of flexible and high-performance surveillance systems. These systems must continuously integrate new sensors and sensing algorithms, which are autonomous (e.g., capable of making decisions independently of a central system) and possess interaction skills (e.g., capable of exchanging observations). For this purpose, our work proposes adopting an agent-based architecture derived from an organizational and holonic (i.e., system of systems) multi-agent model. It leverages autonomous processing methods, resulting in a scalable and modular multisensor and multimethod surveillance systems. A vehicle tracking case study demonstrates the relevance of our approach in terms of effectiveness and runtime.

Since autonomous driving (AD) is one of the most critical problems in the automobile industry, it has garnered the interest of many academics in recent years. An AD framework or a simulator is used to simulate autonomous vehicles (AVs), it can test modules that an AV needs. There are a large number of researchers studying specific AD simulation tasks, but few studies of systematic AD frameworks and simulators exist. This study distinguishes the functions of AD frameworks and simulators, and it makes a deep study of them, helping promote AV development for researchers, enterprises , and developers. This paper reviews open-source and commercial AD frameworks and simulators, introducing and comparing their features, functionalities, and so on. Additionally, we analyze current research on open-source AD frameworks and simulators according to different applied algorithms and hardware studies and discuss efforts to improve their simulation performance. In the last part, this paper proposes promising research fronts for AD frameworks and simulators for the near future from the viewpoints of hardware deficiencies, AD algorithms, scenario generation, vehicle-to-everything, safety and performance, and co-simulation.

CONTEXT: Computational modeling involves the use of computer simulations and models to study and understand real-world phenomena. Its application is particularly relevant in the study of potential interactions between biological elements. It is a promising approach to understand complex biological processes and predict their behavior under various conditions. METHODOLOGY: This paper is a review of the recent literature on computational modeling of biological systems. Our study focuses on the field of oncology and the use of artificial intelligence (AI) and, in particular, agent-based modeling (ABM), between 2010 and May 2023. RESULTS: Most of the articles studied focus on improving the diagnosis and understanding the behaviors of biological entities, with metaheuristic algorithms being the models most used. Several challenges are highlighted regarding increasing and structuring knowledge about biological systems, developing holistic models that capture multiple scales and levels of organization, reproducing emergent behaviors of biological systems, validating models with experimental data, improving computational performance of models and algorithms, and ensuring privacy and personal data protection are discussed.

Intersections are major bottlenecks for road traffic, as well as the origin of many accidents. Efficient management of traffic at intersections is required to ensure both safety and efficiency. Yet, the traditional solutions (static signs, traffic lights) are limited in their efficiency as they consider the flow of vehicles and not the vehicles at the microscopic level. By using inter-vehicular communication of connected vehicles, recent works have shown the possibility to have a great increase in the number of evacuated vehicles thanks to the possibility to give an individual right-of-way directly to each vehicle. In this context of intersections of cooperative vehicles, the scheduling of this right-of-way in order to maximize the throughput of the intersection is still a challenging task, with regard to the hybrid and dynamic aspects of the problem. In this paper, we propose an approach based on Deep Reinforcement Learning (DRL) to efficiently distribute the right-of-way to each vehicle. A Markov Decision Process model of intersections of cooperative vehicles, enabling the application of DRL, is proposed. The performance of the DRL-based scheduling is then compared with classic traffic lights, and with two state-of-the-art cooperative scheduling policies, showing the benefits of the approach (increase of the flow, reduction of CO2 emissions).

International audience

Recently, the civilian applications of Unmanned Aerial Vehicles (UAVs) are gaining more interest in several domains. Due to operational costs, safety concerns, and legal regulations, Agent-Based Simulation (ABS) is commonly used to design models and conduct tests. This has resulted in numerous research works addressing ABS in civilian UAV applications. This paper aims to provide a comprehensive overview of the ABS contribution in civilian UAV applications by conducting a Systematic Literature Review (SLR) on the relevant research in the previous ten years. Following the SLR methodology, this objective is broken down into several research questions aiming to (i) understand the evolution of ABS use in civilian UAV applications and identify the related hot research topics, (ii) identify the underlying artificial intelligence systems used in the literature, (iii) understand how and when ABS is integrated in broader and more complex internet of things & ubiquitous computing environments, and (iv) identity the communication technologies, tools, and evaluation techniques used to design, implement, and test the proposed ABS models. From the SLR results, key research directions are highlighted including problems related to autonomy, explainability, security, flight duration, integration within smart cities, regulations, and validation & verification of the UAV behavior.

The construction of ad-hoc design processes following the Situational Method Engineering (SME) paradigm is currently carried out by adopting a set of phases for which, until now, no well defined techniques and guidelines have been established. The consequence is that organizations are very dependent on method designers' skills. In this paper, we propose an approach based on SME for constructing customized agent oriented design processes. Our approach adopts the metamodel as the most important factor leading to the selection and assembly of method fragments and an algorithm for establishing the instantiation order of metamodel elements. The algorithm makes the proposed approach repeatable and usable even by not very skilled personnel, thus proposing an improvement to the actual situation. The proposed approach and the algorithm are also experimented through the construction of a design process (ASPECS) for developing dynamic hierarchical societies of agents. The approach we created is general enough to be applied in other development contexts (not only agent-oriented).