Progressive automation to gain appropriate trust in management automation systems

This document summarizes the results of the Working Group 5 - ``Progressive Automation / Trust\u27\u27 - at the Dagstuhl Seminar 09201 ``Self-Healing and Self-Adaptive Systems\u27\u27 (organized by A. Andrzejak, K. Geihs, O. Shehory and J. Wilkes). The seminar was held from May 10th 2009 to May 15th 2009 in Schloss Dagstuhl~--~Leibniz Center for Informatics

Delinquent Scheduled Maintenance on Long-Life Vehicles

In 2014, an audit by the Postal Service’s Office of Inspector General found the current fleet of long-life vehicles can only meet the agency’s delivery needs through fiscal year 2017. With the next generation of vehicles still in prototype, the Postal Service lacks assurance that its maintenance strategy to can assure continuity of operations in the interim. Given the urgent need to sustain the fleet in the short-term, this policy memorandum considers incentives as an option to address compliance with scheduled maintenance.

Hyperparameter Optimization for Multi-Objective Reinforcement Learning

Reinforcement learning (RL) has emerged as a powerful approach for tackling complex problems. The recent introduction of multi-objective reinforcement learning (MORL) has further expanded the scope of RL by enabling agents to make trade-offs among multiple objectives. This advancement not only has broadened the range of problems that can be tackled but also created numerous opportunities for exploration and advancement. Yet, the effectiveness of RL agents heavily relies on appropriately setting their hyperparameters. In practice, this task often proves to be challenging, leading to unsuccessful deployments of these techniques in various instances. Hence, prior research has explored hyperparameter optimization in RL to address this concern. This paper presents an initial investigation into the challenge of hyperparameter optimization specifically for MORL. We formalize the problem, highlight its distinctive challenges, and propose a systematic methodology to address it. The proposed methodology is applied to a well-known environment using a state-of-the-art MORL algorithm, and preliminary results are reported. Our findings indicate that the proposed methodology can effectively provide hyperparameter configurations that significantly enhance the performance of MORL agents. Furthermore, this study identifies various future research opportunities to further advance the field of hyperparameter optimization for MORL.Comment: Presented at the MODeM workshop https://modem2023.vub.ac.be/

Estrategias de Green Marketing para la sustentabilidad de los tesoros de la Ciénaga Grande de Santa Marta

El presente trabajo pretende indagar sobre las estrategias de Green marketing para sustentabilidad de los tesoros de la Ciénaga Grande de Santa Marta. Identificara en el desarrollo del turismo que se evidencia en la actualidad generando la utilización del Green marketing como nueva tendencia mundial, surgido desde la misma naturaleza, o si se desarrolla los tipos de estrategia de Green marketing para sustentabilidad de la conservación que las distintas actividades ejecutadas en la Ciénaga impacten al turista especializado en la reserva natural. Este informe de investigación, si bien trata un caso de estudio particular, puede servir de antecedente para futuros análisis similares en el Green marketing desarrollo de las zonas naturales que involucren reservas de Biosferas. Palabras clave: Sustentabilidad, desarrollo, turismo, Green Marketing, reservas de Biosfer

Design and analysis of an E-Puck2 robot plug-in for the ARGoS simulator

peer reviewedIn this article we present a new plug-in for the ARGoS swarm robotic simulator to implement the E-Puck2 robot model, including its graphical representation, sensors and actuators. We have based our development on the former E-Puck robot model (version 1) by upgrading the existing sensors (proximity, light, ground, camera, and battery) and adding new ones (time of flight and simulated encoders) implemented from scratch. We have adapted the values produced by the proximity, light and ground sensors, including the E-Puck2's onboard camera according to its resolution, and proposed four new discharge models for the battery. We have evaluated this new plug-in in terms of accuracy and efficiency through comparisons with real robots and extensive simulations. In all our experiments the proposed plug-in has worked well showing high levels of accuracy. The observed increment of execution times when using the studied sensors varies according to the number of robots and types of sensors included in the simulation, ranging from a negligible impact to 53% longer simulations in the most demanding cases.R-AGR-3933 - C20/IS/14762457/ADARS (01/05/2021 - 30/04/2024) - DANOY Grégoir

Internet of Unmanned Aerial Vehicles—A Multilayer Low-Altitude Airspace Model for Distributed UAV Traffic Management

The rapid adoption of Internet of Things (IoT) has encouraged the integration of new connected devices such as Unmanned Aerial Vehicles (UAVs) to the ubiquitous network. UAVs promise a pragmatic solution to the limitations of existing terrestrial IoT infrastructure as well as bring new means of delivering IoT services through a wide range of applications. Owning to their potential, UAVs are expected to soon dominate the low-altitude airspace over populated cities. This introduces new research challenges such as the safe management of UAVs operation under high traffic demands. This paper proposes a novel way of structuring the uncontrolled, low-altitude airspace, with the aim of addressing the complex problem of UAV traffic management at an abstract level. The work, hence, introduces a model of the airspace as a weighted multilayer network of nodes and airways and presents a set of experimental simulation results using three UAV traffic management heuristics

An Evolutionary Algorithm to Optimise a Distributed UAV Swarm Formation System

In this article, we present a distributed robot 3D formation system optimally parameterised by a hybrid evolutionary algorithm (EA) in order to improve its efficiency and robustness. To achieve that, we first describe the novel distributed formation algorithm3 (DFA3), the proposed EA, and the two crossover operators to be tested. The EA hyperparameterisation is performed by using the irace package and the evaluation of the three case studies featuring three, five, and ten unmanned aerial vehicles (UAVs) is performed through realistic simulations by using ARGoS and ten scenarios evaluated in parallel to improve the robustness of the configurations calculated. The optimisation results, reported with statistical significance, and the validation performed on 270 unseen scenarios show that the use of a metaheuristic is imperative for such a complex problem despite some overfitting observed under certain circumstances. All in all, the UAV swarm self-organised itself to achieve stable formations in 95% of the scenarios studied with a plus/minus ten percent tolerance

Optimising Autonomous Robot Swarm Parameters for Stable Formation Design

Autonomous robot swarm systems allow to address many inherent limitations of single robot systems, such as scalability and reliability. As a consequence, these have found their way into numerous applications including in the space and aerospace domains like swarm-based asteroid observation or counter-drone systems. However, achieving stable formations around a point of interest using different number of robots and diverse initial conditions can be challenging. In this article we propose a novel method for autonomous robots swarms self-organisation solely relying on their relative position (angle and distance). This work focuses on an evolutionary optimisation approach to calculate the parameters of the swarm, e.g. inter-robot distance, to achieve a reliable formation under different initial conditions. Experiments are conducted using realistic simulations and considering four case studies. The results observed after testing the optimal configurations on 72 unseen scenarios per case study showed the high robustness of our proposal since the desired formation was always achieved. The ability of self-organise around a point of interest maintaining a predefined fixed distance was also validated using real robots