With the support of satellite edge computing, Internet of Vehicles (IoV) are utilized more and more widely, especially in ground network unreachable situations. To support more complex edge computing services, the agg...
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With the support of satellite edge computing, Internet of Vehicles (IoV) are utilized more and more widely, especially in ground network unreachable situations. To support more complex edge computing services, the aggregation of satellite computing resources becomes a possible solution. This paper models the aggregated resource management to be a non-cooperative two-stage Stackelberg game framework, and a distributed algorithm is proposed to speed up the convergence of resource allocation. Simulation results demonstrate that the proposed method can acquire maximum utilities of both ground vehicles and satellites.
Traditionally, based on convexity, multi-agent decision-making models can hardly handle scenarios where agents’ cost functions defy this assumption, which is specifically required to ensure the existence of several e...
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ISBN:
(数字)9798350316339
ISBN:
(纸本)9798350316346
Traditionally, based on convexity, multi-agent decision-making models can hardly handle scenarios where agents’ cost functions defy this assumption, which is specifically required to ensure the existence of several equilibrium concepts. More recently, the advent of machine learning (ML), with its inherent non-convexity, has changed the conventional approach of pursuing convexity at all costs. This paper explores and integrates the robustness of game theoretic frameworks in managing conflicts among agents with the capacity of ML approaches, such as deep neural networks (DNNs), to capture complex agent behaviors. Specifically, we employ feed-forward DNNs to characterize agents’ best response actions rather than modeling their goals with convex functions. We introduce a technical assumption on the weight of the DNN to establish the existence and uniqueness of Nash equilibria and present two distributed algorithms based on fixed-point iterations for their computation. Finally, we demonstrate the practical application of our framework to a noncooperative community of smart energy users under a dynamic time-of-use energy pricing scheme.
We study the hypothesis testing problem where an agent seeks to select a hypothesis from a finite set $\Theta$ , based on a sequence of i.i.d. observations of a random variable $X_{k}\sim P^{\ast}$ for $k\geq 1$ with ...
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ISBN:
(数字)9798350354058
ISBN:
(纸本)9798350354065
We study the hypothesis testing problem where an agent seeks to select a hypothesis from a finite set $\Theta$ , based on a sequence of i.i.d. observations of a random variable $X_{k}\sim P^{\ast}$ for $k\geq 1$ with unknown distribution $P^{\ast}$ . Each hypothesis $\theta^{\ast}\in\Theta$ states that $X_{k}\sim P_{\theta}$ . The objective is to find a hypothesis $\theta^{\ast}$ such that $\theta= \arg\min\nolimits_{\theta\in\Theta}D_{KL}(P^{\ast}\Vert P_{\theta})$ . However, the set of hypotheses $\{P_{\theta}\}$ is partially known, where only a finite number of observations are available for the random variables $Y_{s}^{\theta}\sim P_{\theta}$ with $\theta\in \Theta$ . We show that contrary to classical Bayesian approaches, the obtained estimator will not be consistent, and the aggregated log-likelihood ratios will converge in distribution to a Gaussian distribution even when $k\rightarrow\infty$ . Our result states that estimators with uncertain likelihoods will not concentrate on the true hypothesis. There is a strictly positive probability that the belief in a suboptimal hypothesis is maximal.
This paper considers distributed nonconvex op-timization for minimizing the average of local cost functions, by using local information exchange over undirected communication networks. Since the communication channels...
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ISBN:
(数字)9781665467612
ISBN:
(纸本)9781665467629
This paper considers distributed nonconvex op-timization for minimizing the average of local cost functions, by using local information exchange over undirected communication networks. Since the communication channels often have limited bandwidth or capacity, we first introduce a quantization rule and an encoder/decoder scheme to reduce the transmission bits. By integrating them with a distributed algorithm, we then propose a distributed quantized nonconvex optimization algorithm. Assuming the global cost function satisfies the Polyak– Łojasiewicz condition, which does not require the global cost function to be convex and the global minimizer is not necessarily unique, we show that the proposed algorithm linearly converges to a global optimal point. Moreover, a low data rate is shown to be sufficient to ensure linear convergence when the algorithm parameters are properly chosen. The theoretical results are illustrated by numerical simulation examples.
Low-carbon buildings (LCBs) are normally equipped with distributed energy resources (DERs), thereby creating LCB prosumers with capacities for energy production and consumption. Peer-to-Peer (P2P) energy trading among...
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ISBN:
(数字)9798350381832
ISBN:
(纸本)9798350381849
Low-carbon buildings (LCBs) are normally equipped with distributed energy resources (DERs), thereby creating LCB prosumers with capacities for energy production and consumption. Peer-to-Peer (P2P) energy trading among LCB prosumers could bring higher economic benefits for themselves. To fully harness the potential benefits of LCBs in P2P energy trading, an asynchronous P2P energy trading method among heterogeneous LCB prosumers is proposed in this paper. The flexibility of heating loads of LCBs is fully exploited to benefit LCB prosumers in P2P energy trading by using the thermal dynamics of LCBs. Additionally, each LCB prosumer is heterogeneous in terms of the energy resources configuration and communication network infrastructure, which causes the heavy computation and communication burden using the traditional solution method. To improve the computational efficiency, an asynchronous distributed algorithm based on alternating direction method of multipliers (ADMM) is introduced to enable each LCB prosumer to trade energy asynchronously with no need to wait for the trading information from other LCB prosumers with poor communication network infrastructure. This asynchronous procedure significantly reduces the computation time of P2P energy trading. Simulation results verify the effectiveness of the proposed method and the feasibility of the proposed algorithm.
In the era of increasing automation, systems are making more autonomous decisions than ever before, often leading to conflicts with other systems. In situations where humans are entangled in such conflicts without pro...
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ISBN:
(数字)9798400705960
ISBN:
(纸本)9798350388701
In the era of increasing automation, systems are making more autonomous decisions than ever before, often leading to conflicts with other systems. In situations where humans are entangled in such conflicts without proper explanation, they experience frustration. The consideration of the human’s frustration level in such scenarios therefore is a key aspect of solving these conflicts. Our approach involves synthesizing an appropriate supervisory control system (SCS) within a gaming context. The main focus of this paper is to seamlessly blend a SCS with conflict resolution capabilities, capitalizing on the explanatory potential. The central objective is to explore how this integration can be accomplished by employing the SCS as a game master or as a moderator within a game-theoretic framework. In this capacity, the system becomes a central authority equipped with comprehensive information relevant to the game, possessing the knowledge and insights required to navigate intricate scenarios. Employing a game-theoretic approach allows us to harness the system’s in-depth understanding of the game’s dynamics to strategically resolve conflicts and optimize outcomes. The game master utilises an explanation model designed to provide understandable and situationally aware explanations, effectively mitigating frustration levels for humans involved in the *** CONCEPTS • Theory of computation→Algorithmic game theory; • Humancentered computing→User models; • Computing methodologies→ Modeling methodologies.
In this paper, we investigate the distributed $L_{2}-L_{\infty}$ containment control problem of multi agent systems with a desired performance index. The objective is to use local information to make all followers s...
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ISBN:
(数字)9789887581536
ISBN:
(纸本)9781665482561
In this paper, we investigate the distributed $L_{2}-L_{\infty}$ containment control problem of multi agent systems with a desired performance index. The objective is to use local information to make all followers stay in the convex hull formed by multiple stationary leaders while satisfying a desired $L_{2}-L_{\infty}$ performance index in the presence of external disturbances. Based on Lyapunov function, some sufficient conditions are given to solve containment control problem with the desired $L_{2}-L_{\infty}$ performance.
The domination problem is one of the fundamental graph problems and there are many variations. The problem has practical applications in a distributed setting, and studied well in the distributed computing community. ...
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The domination problem is one of the fundamental graph problems and there are many variations. The problem has practical applications in a distributed setting, and studied well in the distributed computing community. In this paper, we propose a new problem called the minus ( $L, K, Z$ ) -domination problem where $L, K$ , and $Z$ are integers such that $L\leq-1,K\geq 1$ , and $Z\geq 1$ . The minus ( $L, K, Z$ ) -domination problem is a problem to assign a value $L, L+1, \ldots, 0, \ldots, K-1, K$ for each vertex in a graph such that the local summation of values is greater than or equal to $Z$ . Because it is the same as the minus domination problem when $L=-1, K=1$ and $Z=1$ , it is an extension of the minus domination problem. Then, we propose a self-stabilizing distributed algorithm for the minus ( $L, K, Z$ ) -domination problem, where self-stabilization is a class of fault-tolerant distributed algorithms that tolerate arbitrary finite number of transient faults. The proposed algorithm is designed under the distance-2 model and the unfair central daemon, and its convergence time is $O(n)$ , that is, linear to $n$ , where $n$ is the number of processes. If it is converted into the ordinary distance-1 model with a transformer, we obtain an algorithm whose convergence time is $O(nm)$ .
This study focuses on aggregative games, a type of Nash games that is played over a network. In these games, the cost function of an agent is affected by its own choice and the sum of all decision variables of the pla...
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ISBN:
(数字)9781665467612
ISBN:
(纸本)9781665467629
This study focuses on aggregative games, a type of Nash games that is played over a network. In these games, the cost function of an agent is affected by its own choice and the sum of all decision variables of the players involved. We consider a distributed algorithm over a network, whereby to reach a Nash equilibrium point, each agent maintains a prediction of the aggregate decision variable and share it with its local neighbors over a strongly connected directed network. The existing literature provides such algorithms for undirected graphs which typically require the use doubly stochastic weight matrices. We consider a fixed directed communication network and investigate a synchronous distributed gradient-based method for computing a Nash equilibrium. We provide convergence analysis of the method showing that the algorithm converges to the Nash equilibrium of the game, under some standard conditions.
We study robust mean estimation in an online and distributed scenario in the presence of adversarial data attacks. At each time step, each agent in a network receives a potentially corrupted data point, where the data...
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ISBN:
(数字)9781665467612
ISBN:
(纸本)9781665467629
We study robust mean estimation in an online and distributed scenario in the presence of adversarial data attacks. At each time step, each agent in a network receives a potentially corrupted data point, where the data points were originally independent and identically distributed samples of a random variable. We propose online and distributed algorithms for all agents to asymptotically estimate the mean. We provide the error-bound and the convergence properties of the estimates to the true mean under our algorithms. Based on the network topology, we further evaluate each agent’s trade-off in convergence rate between incorporating data from neighbors and learning with only local observations.
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