The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Coordination problems including miscoordination and relative overgeneralization are difficult to overcome especially in dynamic and stochastic environments. In the practical scenario, there may be a large number of agents, and the interactions between agents may be sparse and unfixed. In this paper, we study the coordination problems and stochastic rewards under the social learning framework where...
For task completion in distributed environments, a set of resources is required and a group of agents must cooperate in deciding the share each should provide to maximize the system performance. We address the problem from an evolutionary game-theoretic perspective and present a fully distributed algorithm based on local replicator dynamics. By using the optimality condition, we prove the convergence...
In this paper, we present a new adaptive multiflow routing algorithm to select end-to-end paths in packetswitched networks. This algorithm provides provable optimality guarantees in the following game theoretic sense: The network configuration converges to a configuration arbitrarily close to a pure Nash equilibrium. In this context, a Nash equilibrium is a configuration in which no flow can improve...
Conspiracy Number Search (CNS) is a MIN/MAX tree search algorithm, trying to guarantee the accuracy of the MIN/MAX value of a root node. It suffers from a low efficiency because of its slow convergence and a big cost of computing conspiracy numbers. However, the conspiracy number is still a promising concept for measuring the “stability”, which can be used to analyze game progress patterns. In this...
Slot machines are the most popular facility in casinos worldwide. With the advancement of computer technology, the operating reel spinning of the current slot machine is presented by computer software emulation instead of rotating mechanical iron reels. The reel strip table of a slot machine has many special pictures embedded for different attractive themes. Each slot machine achieves a hit rate based...
This paper investigates distributed resource allocation in next-generation underlay Device-to-Device (D2D) networks. The joint channel and power allocation for a D2D network underlaying a cellular network is formulated as a non-cooperative game. A utility-based learning algorithm which does not require information exchange between device pairs is proposed to determine the channel index and power level...
This paper concentrates on seeking the generalized Nash equilibria of network aggregative games by using a distributed continuous-time algorithm. By considering the variational inequality related to the problem, we design a distributed algorithm seeking the variational equilibria, which are practically an essential part of generalized Nash equilibrium points. Then the novel distributed projected continuous-time...
Alternating direction method of multipliers (ADMM) is a promising approach to solve “big data” problems due to its efficient variable decomposition and fast convergence. However, it is subject to the following two fundamental assumptions: no contradiction among multiple controllers' objectives and ideal feedback from the agents to the controllers. In this paper, a multiple-leader multiple-follower...
In this paper, distributed Nash equilibrium seeking for multi-agent games, particularly for games where the players' payoff functions are partially coupled, is investigated. To model the (partial, explicit) dependence of the players' payoff functions on the players' actions, an interference graph is introduced. Besides, the players are supposed to be equipped with a communication graph to achieve...
In this paper, a fully distributed strategy is proposed to solve the N-coalition multi-agent games. The agents in the considered N-coalition multi-agent games are supposed to have limited access to the other players' actions. Consensus protocols, including a leader-following consensus protocol and a dynamic average consensus protocol, are leveraged to search for the Nash equilibrium of the N-coalition...
In this paper, we investigate a distributed Nash equilibrium seeking problem for a class of aggregative games that the strategic interaction is characterized by a sum of nonlinear mapping of heterogeneous local decisions. We consider non-quadratic local cost functions and constrained strategy sets. We propose a novel continuous-time distributed algorithm for equilibrium seeking based on dynamic average...
The dynamics of two-word naming game incorporating the influence of biased assimilation is investigated in this paper. Firstly an extended naming game with biased assimilation (NGBA) is proposed. The hearer in NGBA accepts the received information in a biased manner, where he will refuse to accept the conveyed word with a predefined probability, if it is different from his own current memory. Secondly,...
Controlling the motion of a group of unmanned flight vehicles (FVs) in a perturbed environment is considered on the example of two similar problems: tracking of the dynamic target and moving along the given path. The problem of the target tracking implies that a randomly-arranged FV group approaches close to the target and flies near it during a specified time period. The low-velocity target seeks...
Opponent modeling is an essential approach for building competitive computer agents in imperfect information games. This paper presents a novel approach to accelerate the convergence process in opponent modeling. The approach applies neural network (ANN) to abstract and build an endgame data set of imperfect information game. Based on a labeled database of author's previous work, several parameters...
Under the background of the rapid development and the continuous exposure of risks in peer to peer lending platforms industry in China, this paper attempts to construct an evolutionary game model between the peer to peer lending platforms and the regulators, to analyze the evolution and stability of the two groups under different circumstances. The results show that the convergence state of peer to...
Multi-agent reinforcement learning has been paid much attention due to its wide applications in various engineering systems. In this paper, the control problems of large-scale multi-agent systems with multiple roles are formulated into a multiplayer Stackelberg game, which provides a new perspective on cooperative issues. Then a Stackelberg Q-learning algorithm is proposed and knowledge transfer is...
We present a novel solution for Channel Assignment Problem (CAP) in Device-to-Device (D2D) wireless networks that takes into account the throughput estimation noise. CAP is known to be NP-hard in the literature and there is no practical optimal learning algorithm that takes into account the estimation noise. In this paper, we first formulate the CAP as a Stochastic Optimization Problem (SOP) to maximize...
In still developing, public cloud-computing markets, prices for virtual machine (VM) offerings fluctuate, and not just for spot/preemptible instances. Moreover, some (particularly derivative) providers allow for fine-grain initial resource provisioning and dynamic reprovisioning of VMs. In this preliminary study, we consider long-lived tenants of a public cloud under resource-based service-level agreements...
In this paper, we propose a general pricing framework, helping the controller promote agents to achieve its objective, for a big data network with one controller and a large number of agents. The convergence of the framework is guaranteed for a general class of objective functions: a separable convex function for the controller and a convex function for each agent. Specially, the proposed framework...
System performance on cognitive femtocell network depends on power control method. Therefore, power control comparison between DPC and PCG is needed. Result showed that DPC had higher convergence rate than PCG but maximum values of DPC was only equal to SINR target. The proposed PCG at the convergent condition was able to exceed the SINR target, yet it had higher power than the previous PCG which...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.