Xiaotong Cheng

The nodes’ interconnections on a social network often reflect their dependencies and information-sharing behaviors. Nevertheless, abnormal nodes, which significantly deviate from most of the network concerning patterns or behaviors, can lead to grave consequences. Therefore, it is imperative to design efficient online learning algorithms that robustly learn users’ preferences while simultaneously detecting anomalies. We introduce a novel bandit algorithm to address this problem. Through network knowledge, the method characterizes the users’ preferences and residuals of feature information. By learning and analyzing these preferences and residuals, it develops a personalized recommendation strategy for each user and simultaneously detects anomalies. We rigorously prove an upper bound on the regret of the proposed algorithm and experimentally compare it with several state-of-the-art collaborative contextual bandit algorithms on both synthetic and real-world datasets.

In this article, we investigate the online influence maximization in social networks. Most prior research studies on online influence maximization assume that the nodes are fully cooperative and act according to their stochastically generated influence probabilities on others. In contrast, we study the online influence maximization problem in the presence of some corrupted nodes whose damaging effects diffuse throughout the network. We propose a novel bandit algorithm, CW-IMLinUCB, which robustly learns and finds the optimal seed set in the presence of corrupted users. Theoretical analyses establish that the regret performance of our proposed algorithm is better than the state-of-the-art online influence maximization algorithms. Extensive empirical evaluations on synthetic and real-world datasets also show the superior performance of our proposed algorithm.

Modern power systems integrate renewable distributed energy resources (DERs) as an environment-friendly enhancement to meet the ever-increasing demands. However, the inherent unreliability of renewable energy renders developing DER management algorithms imperative. We study the energy-sharing problem in a system consisting of several DERs. Each agent harvests and distributes renewable energy in its neighborhood to optimize the network's performance while minimizing energy waste. We model this problem as a bandit convex optimization problem with constraints that correspond to each node's limitations for energy production. We propose distributed decision-making policies to solve the formulated problem, where we utilize the notion of dynamic regret as the performance metric. We also include an adjustment strategy in our developed algorithm to reduce the constraint violations. Besides, we design a policy that deals with the non-stationary environment. Theoretical analysis shows the effectiveness of our proposed algorithm. Numerical experiments using a real-world dataset show superior performance of our proposal compared to state-of-the-art methods.

We study a structured multi-agent multi-armed bandit (MAMAB) problem in a dynamic environment. A graph reflects the information-sharing structure among agents, and the arms' reward distributions are piecewise-stationary with several unknown change points. The agents face the identical piecewise-stationary MAB problem. The goal is to develop a decision-making policy for the agents that minimizes the regret, which is the expected total loss of not playing the optimal arm at each time step. Our proposed solution, Restarted Bayesian Online Change Point Detection in Cooperative Upper Confidence Bound Algorithm (RBO-Coop-UCB), involves an efficient multi-agent UCB algorithm as its core enhanced with a Bayesian change point detector. We also develop a simple restart decision cooperation that improves decision-making. Theoretically, we establish that the expected group regret of RBO-Coop-UCB is upper bounded by $\mathcal {O}(KNM\log T + K\sqrt{MT\log T})$ , where $K$ is the number of agents, $M$ is the number of arms, and $T$ is the number of time steps. Numerical experiments on synthetic and real-world datasets demonstrate that our proposed method outperforms the state-of-the-art algorithms.

Fog computing leverages the task offloading capabilities at the network’s edge to improve efficiency and enable swift responses to application demands. However, the design of task allocation strategies in a fog computing network is still challenging because of the heterogeneity of fog nodes and uncertainties in system dynamics. We formulate the distributed task allocation problem as a social-concave game with bandit feedback and show that the game has a unique Nash equilibrium, which is implementable using no-regret learning strategies (regret with sublinear growth). We then develop two no-regret online decision-making strategies. One strategy, namely bandit gradient ascent with momentum, is an online convex optimization algorithm with bandit feedback. The other strategy, Lipschitz bandit with initialization, is an EXP3 multi-armed bandit algorithm. We establish regret bounds for both strategies and analyze their convergence characteristics. Moreover, we compare the proposed strategies with an allocation strategy named learning with linear rewards. Theoretical-and numerical analysis shows the superior performance of the proposed strategies for efficient task allocation compared to the state-of-the-art methods.

Fog computing has emerged as a potential solution to accommodate the explosive computational demand of mobile users. This potential mainly stems from the capacity of task offloading and allocation at the network edge, reducing the delay and improving the quality of service. However, optimizing the performance of a fog network is often challenging, when consider the distinct abilities and capacities of fog nodes. We study the distributed task allocation problem in such a heterogeneous fog computing network under noises. We formulate the problem as a social-concave game, where the players attempt to minimize their regret while converging to Nash equilibrium. We develop a no-regret strategy for task allocation. The strategy, namely bandit gradient ascent with momentum, is an online convex optimization algorithm with bandit feedback. Theoretical and numerical analysis show the superior performance of the proposed strategy for efficient task allocation compared to the state-of-the-art methods.

In this article, we present one solution to the multitask formation and moving objects interception problem of autonomous surface vehicles (ASVs) using the graph-based approach. The investigated problem includes three subtasks, i.e., to steer agents to a desired geometric pattern, to assign the ASVs for intercepting the moving attacker and forming a new formation, respectively, and to revert to original desired formation after successful interception. A switching strategy is designed to make the ASV switch between different modes. To deal with the nonlinear dynamics of ASV and improve the robustness against model uncertainties and external disturbances, the adaptive backstepping technique is incorporated. It is shown that the group of ASVs can accomplish all the tasks with different courses of attackers. The work offers a more flexible solution for the ASV group where malicious attacker exists. The effectiveness of the proposed approach is illustrated by simulations.