Multi-UAV Conflict Resolution with Graph Convolutional Reinforcement Learning

Safety is the primary concern when it comes to air traffic. In-flight safety between Unmanned Aircraft Vehicles (UAVs) is ensured through pairwise separation minima, utilizing conflict detection and resolution methods. Existing methods mainly deal with pairwise conflicts, however, due to an expected increase in traffic density, encounters with more than two UAVs are likely to happen. In this paper, we model multi-UAV conflict resolution as a multiagent reinforcement learning problem. We implement an algorithm based on graph neural networks where cooperative agents can communicate to jointly generate resolution maneuvers. The model is evaluated in scenarios with 3 and 4 present agents. Results show that agents are able to successfully solve the multi-UAV conflicts through a cooperative strategy.

The Internet of Cooperative Agents Architecture (X-IoCA) for Robots, Hybrid Sensor Networks, and MEC Centers in Complex Environments: A Search and Rescue Case Study

Cloud robotics and advanced communications can foster a step-change in cooperative robots and hybrid wireless sensor networks (H-WSN) for demanding environments (e.g., disaster response, mining, demolition, and nuclear sites) by enabling the timely sharing of data and computational resources between robot and human teams. However, the operational complexity of such multi-agent systems requires defining effective architectures, coping with implementation details, and testing in realistic deployments. This article proposes X-IoCA, an Internet of robotic things (IoRT) and communication architecture consisting of a hybrid and heterogeneous network of wireless transceivers (H2WTN), based on LoRa and BLE technologies, and a robot operating system (ROS) network. The IoRT is connected to a feedback information system (FIS) distributed among multi-access edge computing (MEC) centers. Furthermore, we present SAR-IoCA, an implementation of the architecture for search and rescue (SAR) integrated into a 5G network. The FIS for this application consists of an SAR-FIS (including a path planner for UGVs considering risks detected by a LoRa H-WSN) and an ROS-FIS (for real-time monitoring and processing of information published throughout the ROS network). Moreover, we discuss lessons learned from using SAR-IoCA in a realistic exercise where three UGVs, a UAV, and responders collaborated to rescue victims from a tunnel accessible through rough terrain.

Coalition Formation among the Cooperative Agents for Efficient Energy Consumption

Energy saving is a significant research area in Saudi Arabia; however, significant problems have emerged related to its distribution and consumption. Use of an agent is assumed to combat these problems by forming efficient coalitions to control the energy consumption and energy distribution process. This study presents a novel algorithm for distributing the value calculation among the cooperative agents. This is likely to reduce the consumption of energy and extend the coalition lifetime used. The developed algorithm is compared with the basic modified coalition formation algorithm for evaluating its effectiveness. The results showed a reduction in cooling consumption by 20% after applying optimization algorithms. The amount of reduction in the cooling consumption reflects a 31% reduction in expected cooling costs, without affecting the household comfort. Therefore, the study concludes that DNsys provided better performance than the NNsys.

TIDOT: A Teacher Imitation Learning Approach for Domain Adaptation with Optimal Transport

Using the principle of imitation learning and the theory of optimal transport we propose in this paper a novel model for unsupervised domain adaptation named Teacher Imitation Domain Adaptation with Optimal Transport (TIDOT). Our model includes two cooperative agents: a teacher and a student. The former agent is trained to be an expert on labeled data in the source domain, whilst the latter one aims to work with unlabeled data in the target domain. More specifically, optimal transport is applied to quantify the total of the distance between embedded distributions of the source and target data in the joint space, and the distance between predictive distributions of both agents, thus by minimizing this quantity TIDOT could mitigate not only the data shift but also the label shift. Comprehensive empirical studies show that TIDOT outperforms existing state-of-the-art performance on benchmark datasets.

Improving Effectiveness of Rural Information and Communication Technology Offices: The Case of Qazvin Province in Iran

The information and communication technology (ICT) offices in rural areas of Iran have been developed as government provided counters under a national project in the past decades. The rural ICT offices were expected to benefit the rural people in various socio-economic dimensions such as health, social connectivity, crop diversity, agricultural productivity, occupational capability and the lifestyle in general. However, these middle range offices in Iran did not perform as expected, and thus they require an urgent restructuring to boost up their performances and to enhance their acceptability. This study investigates the effectiveness of the ICT systems and services in place in the Qazvin province of Iran with the purpose of identifying the major requirements needed to fix up the system. The focus of this study was around 10,000 people organised through rural ICT agents and their users in the rural area of Qazvin. The survey involves 138 rural ICT offices operated by 103 cooperative agents. Of them, 16 rural ICT offices were selected randomly, and 165 rural users connected with the selected offices were interviewed by the research team. Collected data have been analysed with structural equation modeling. The study shows that education, policy and management requirements deserve the highest attention, and therefore the best ways to improve the effectiveness of rural ICT offices. This study suggests that the effectiveness of rural ICT offices can be improved significantly through providing in-service education for ICT experts, arranging regular training programme for ICT office agents and using mass media to educate villagers on various aspects of ICTs.

SHOULD THE GOVERNMENT REWARD COOPERATION? INSIGHTS FROM AN AGENT-BASED MODEL OF WEALTH REDISTRIBUTION

In our multi-agent model, agents generate wealth from repeated interactions for which a prisoner’s dilemma payoff matrix is assumed. Their gains are taxed by a government at a rate [Formula: see text]. The resulting budget is spent to cover administrative costs and to pay a bonus to cooperative agents, which can be identified correctly only with a probability [Formula: see text]. Agents decide at each time step to choose either cooperation or defection based on different information. In the local scenario, they compare their potential gains from both strategies. In the global scenario, they compare the gains of the cooperative and defective subpopulations. We derive analytical expressions for the critical bonus needed to make cooperation as attractive as defection. We show that for the local scenario the government can establish only a medium level of cooperation because the critical bonus increases with the level of cooperation. In the global scenario, instead full cooperation can be achieved once the cold-start problem is solved because the critical bonus decreases with the level of cooperation. This allows to lower the tax rate, while maintaining high cooperation.

Event-triggered flocking control of multi-agent systems in presence of cyber attacks

We propose an event-triggered control system for multi-agent systems with the double-integrator network to achieve resilient flocking behavior in the presence of cyberattacks. The method can manage connectivity and increase the robustness of the topology graph via a three-state hybrid control. Each state of hybrid control has a distinct triggering condition. The developed event-triggered update rules can mitigate the influence of the noncooperative agents using the weighted mean subsequence reduced algorithm and reduce unnecessary communication among them. As a result, the performance of the system can effectively improve. Convergence of the velocity and orientation of the agents is guaranteed by using this type of control structure and resilient consensus protocol. We assume that the bound on the number of noncooperative agents in the neighbor sets of cooperative agents is known. The proposed scheme with the event-triggering rules can avoid Zeno behaviors inherently. Finally, a simulation example is worked out to demonstrate the effectiveness of the proposed approach.

Robust Distributed Secondary Voltage Restoration Control of AC Microgrids under Multiple Communication Delays

This paper focuses on the robust distributed secondary voltage restoration control of AC microgrids (MGs) under multiple communication delays and nonlinear model uncertainties. The problem is addressed in a multi-agent fashion where the generators’ local controllers play the role of cooperative agents communicating over a network and where electrical couplings among generators are interpreted as disturbances to be rejected. Communications are considered to be affected by heterogeneous network-induced time-varying delays with given upper-bounds and the MG is subjected to nonlinear model uncertainties and abrupt changes in the operating working condition. Robustness against uncertainties is achieved by means of an integral sliding mode control term embedded in the control protocol. Then, the global voltage restoration stability, despite the communication delays, is demonstrated through a Lyapunov-Krasovskii analysis. Given the delays’ bounds, and because the resulting stability conditions result in being non-convex with respect to the controller gain, then a relaxed linear matrix inequalities-based tuning criteria is developed to maximize the controller tuning, thus minimizing the restoration settling-time. By means of that, a criteria to estimate the maximal delay margin tolerated by the system is also provided. Finally, simulations on a faithful nonlinear MG model, showing the effectiveness of the proposed control strategy, are further discussed.

On Nash Equilibria in Non-Cooperative All-Optical Networks

We consider the problem of determining a routing in all-optical networks, in which some couples of nodes want to communicate. In particular, we study this problem from the point of view of a network provider that has to design suitable payment functions for non-cooperative agents, corresponding to the couples of nodes wishing to communicate. The network provider aims at inducing stable routings (i.e., routings corresponding to Nash equilibria) using a low number of wavelengths. We consider three different kinds of local knowledge that agents may exploit to compute their payments, leading to three corresponding information levels. Under complete information, the network provider can design a payment function, inducing the agents to reach a Nash equilibrium mirroring any desired routing. If the price to an agent is computed only as a function of the wavelengths used along connecting paths (minimal level) or edges (intermediate level), the most reasonable functions either do not admit Nash equilibria or admit very inefficient ones, i.e., with the largest possible price of anarchy. However, by suitably restricting the network topology, a constant price of anarchy for chains and rings and a logarithmic one for trees can be obtained under the minimal and intermediate levels, respectively.