Task allocation in multiagent systems is a critical challenge due to the heterogeneity of tasks and agents, where tasks have varying resource requirements and agents possess differing resource supplies. During execution, agents' resources deplete while tasks' requirements dynamically decrease. This problem has broad applications, such as optimally deploying UAVs equipped with diverse medical supplies in disaster rescue scenarios to minimize casualties. To address this, this article proposes a coalition formation game model, formulated as a potential game. We theoretically prove the submodularity of both the coalition utility function and the global utility function. Based on this submodularity, we establish that the efficiency lower bound of any Nash equilibrium in the proposed game model is given by $e / (2e-1)$ , significantly outperforming the 50% bound reported in prior studies. Furthermore, we introduce an inertia-based log-linear learning algorithm enhanced with a multiagent cooperative exchange mechanism, which enables the system to escape from suboptimal equilibria and improve global utility. In addition, we extend the algorithm to accommodate local communication constraints and dynamic allocation scenarios. Extensive experimental evaluations demonstrate that our proposed method achieves superior performance across diverse scenarios compared to existing algorithms.

Federated learning is a distributed machine learning system that uses participants’ data to train an improved global model. In federated learning, participants collaboratively train a global model, and after the training is completed, each participant receives that global model along with an incentive. Rational participants try to maximize their individual utility, and they will not input their high-quality data truthfully unless they are provided with satisfactory payments based on their contributions. Furthermore, federated learning benefits from the cooperation of participants. Accordingly, how to establish an incentive mechanism that both incentivizes inputting data truthfully and promotes cooperative contributions has become an important issue to consider. In this article, we introduce a data sharing game model for federated learning and employ game-theoretic approaches to design a core-selecting incentive mechanism by utilizing a popular concept in cooperative games, the core. In federated learning, the core can be empty, resulting in the core-selecting mechanism becoming infeasible. To address this issue, our core-selecting mechanism employs a relaxation method and simultaneously minimizes the benefits of inputting false data for all participants. Meanwhile, to reduce the computational complexity of the core-selecting mechanism, we propose an efficient core-selecting mechanism based on sampling approximation that only aggregates models on sampled coalitions to approximate the exact result. Extensive experiments demonstrate that the efficient core-selecting mechanism can incentivize truthful input of high-quality data and promote cooperation effectively, while it reduces computational overhead compared to the core-selecting mechanism.

The objective of visual servoing aims to control an object's motion with visual feedbacks and becomes popular recently. Problems of complex modeling and instability always exist in visual servoing methods. Moreover, there are few research works on selection of the servoing gain in image-based visual servoing (IBVS) methods. This paper proposes an IBVS method with Q-Learning, where the learning rate is adjusted by a fuzzy system. Meanwhile, a synthetic preprocess is introduced to perform feature extraction. The extraction method is actually a combination of a color-based recognition algorithm and an improved contour-based recognition algorithm. For dealing with underactuated dynamics of the unmanned aerial vehicles (UAVs), a decoupled controller is designed, where the velocity and attitude are decoupled through attenuating the effects of underactuation in roll and pitch and two independent servoing gains, for linear and angular motion servoing, respectively, are designed in place of single servoing gain in traditional methods. For further improvement in convergence and stability, a reinforcement learning method, Q-Learning, is taken for adaptive servoing gain adjustment. The Q-Learning is composed of two independent learning agents for adjusting two serving gains, respectively. In order to improve the performance of the Q-Learning, a fuzzy-based method is proposed for tuning the learning rate. The results of simulations and experiments on control of UAVs demonstrate that the proposed method has better properties in stability and convergence than the competing methods.