Potential Game Approach Research Articles

A Potential Game Approach to Target Assignment in Heterogeneous Manned/Unmanned Aerial Team With Incomplete Information

This brief proposes a potential game method in a dis-tributed manner to address the target assignment problem with incomplete information in heterogeneous manned/unmanned aer-ial teams. Firstly, a heterogeneous system is formulated as a graph and different utility functions are designed to characterize the util-ities of the two kinds of members. Secondly, a local information matrix is developed for each member to infer the actions of others. Finally, an improved learning algorithm is proposed to achieve the optimal solution. Simulation results show the feasibility and effec-tiveness of the potential game approach with information esti-mates.

Safe and Human-Like Autonomous Driving: A Predictor–Corrector Potential Game Approach

Safe and Human-Like Autonomous Driving: A Predictor–Corrector Potential Game Approach

Multi-objective optimization of clustered wind farms based on potential game approach

With the rapid development of offshore wind power, the construction and operation of multiple adjacent wind farms (WFs) in the form of cluster has become a trend. However, the method for single WF planning can not address the WF cluster layout optimization in the complex relationship of competition or cooperation. This paper proposes a novel method to design clustered offshore WFs. The objectives to be optimized include the maximization of WF generated power and the minimization of wind turbine (WT) turbulence intensity. To solve this problem, a mathematical model based on the potential game theory is established. In this planning model, each WF in the cluster is assumed to be an individual stakeholder, the decision variables and the constraints are mapped to be the strategies and the strategy sets of the stakeholders, respectively, and the objectives are set as their payoffs. The non-dominated sorting genetic algorithm II (NSGA-II) is synthesized with the dependency structure matrix genetic algorithm II (DSMGA-II) to tackle the multi-objective optimization problem (MOOP) and the fuzzy-membership function (FMF) method is used to select the final solution from the Pareto optimal set. The best-strategy-response iterative algorithm is put forward to search for the Nash equilibrium (NE) of the game. Case studies on three clustered offshore WFs in Qidong, China, are carried out to validate the effectiveness of the proposed approach. Also, the simulation results indicate that the synthesized algorithm outperforms the NSGA-II in solving this optimization problem.

A Potential Game Approach for D2D Pairing and Channel Selection for NOMA-Enabled D2D Cellular Networks

A Potential Game Approach for D2D Pairing and Channel Selection for NOMA-Enabled D2D Cellular Networks

Decentralized frequency regulation service provision for virtual power plants: A best response potential game approach

The high-renewable-penetrated power system frequently requires frequency regulation services. By aggregating heterogeneous demand-side flexible resources, the virtual power plants (VPP) are able to quickly respond to the frequency regulation signal, enabling them promising frequency regulation service providers. However, due to the difference in ownership, it is unlikely that the self-interested demand-side resources are willing to follow the VPP-oriented dispatch results. To bridge this gap, this paper proposes a decentralized game-based mechanism for the frequency regulation service provision inside the VPP. Firstly, the supply function-based bidding models of aggregated demand-side resources, namely, demand-side flexible resources and renewable distributed generations, are established. Then, a non-cooperative generalized Nash game for frequency regulation service provision is formulated. The existence, uniqueness, and competitive efficiency loss of the game are analyzed by designing an equivalent best response potential game of the original game. Additionally, a decentralized look-ahead real-time market clearing mechanism for frequency regulation service provision inside the VPP is proposed based on dual decomposition algorithm. Finally, the effectiveness and computation efficiency of the proposed method are validated in the case studies. Meanwhile, it is shown that the competition efficiency loss can be effectively reduced from around 80% to 6% when increasing the demand-side resource population from 20 to 2000 as well as balancing the demand-side resource composition.

Distributed Coverage Control with Mobile Robots: A Potential Game Approach

The use of mobile robots in industrial applications has led to a demand for autonomous multi-robot systems with robust and distributed algorithms. A critical objective in such systems is coverage control, where a team of mobile robots need to respond to spatiotemporal events in a bounded region. Here, we address a specific coverage problem, where a group of mobile robots are tasked with responding to events by covering specific locations on two sides of a linear workstation. We formulate the problem as a game played by the mobile robots with well-designed player strategies, and we demonstrate that the resulting framework is a potential game based on equally shared utilities among the robots. The proposed framework is distributed and decentralized, allowing for anonymous identities and constrained sensing capabilities in the robots. A set of simulation studies verify our approach.

Spectrum Compensation-Based Joint Communication and Jamming Channel Allocation: A Hierarchical Stochastic Potential Game Approach

No doubt both communication and jamming are indispensable in wireless confrontation. For the jamming side, a major challenge is how to coordinate the jamming and the internal communication behaviors. This paper investigates a joint channel strategy optimization problem in an integrated communication and jamming system. A spectrum compensation mechanism is proposed to coordinate the channel strategies between the users and the jammer within a Stackelberg game framework. Considering the dynamics of strategy space, we formulate the followers sub-game as a stochastic potential game. Moreover, we prove the existence of Stackelberg equilibrium and propose an algorithm to search for the equilibrium solution. Simulation results show that the proposed method can increase the jammer's and the users' utilities significantly compared with the uncoordinated situations.

Transient stability constrained distributed optimal dispatch for microgrids: A state‐based potential game approach

AbstractThe increasing proportion of renewable energy generation (REG) brings severe challenges to the optimal dispatch and stability control of microgrids. On the one hand, the dynamics of REG result in new stability problems. On the other hand, it is difficult to harmonize the benefits of distributed generators (DGs) since they often belong to different owners. To this end, this paper proposes a distributed optimal dispatching method using state‐based potential game theory, which considers both system stability and individual economy. First, an optimal dispatching model is constructed for microgrid containing various DGs. The transient stability constraint (TSC) of microgrid is given based on dissipation theory. Then, by treating each node as an agent, the optimization model is converted into a multi‐agent potential game with a state space. And a distributed algorithm is developed which is capable of handling coupled constraints. To implement the proposed method, updating rules are designed with respect to TSC based on Gerschgorin theorem, and the convergence of the algorithm are discussed. Simulation and experimental results show that the proposed method can maximize the benefits of each DG without the participation of control centre, while the ability of the microgrid to resist large disturbances is also guaranteed.

A Potential Game Approach to Multi-UAV Accurate Coverage Based on Deterministic Radio Wave Propagation Model in Urban Area

A Potential Game Approach to Multi-UAV Accurate Coverage Based on Deterministic Radio Wave Propagation Model in Urban Area

A Potential Game Approach for Decentralized Resource Coordination in Coexisting IWNs

To meet the requirements of various emerging manufacturing applications, multiple Industrial Wireless Networks (IWNs) are employed to operate in the same region. However, the limited communication resources inevitably incur interference in the time and frequency domains, which is known as the coexistence problem. Existing centralized mechanisms suffer from a low computational efficiency in a large-scale network scenario, and the globally shared information cannot be fully obtained in practice. To this end, we first design an incomplete information sharing protocol to clarify the decentralized coordination among coexisting IWNs. We then formulate the coexistence problem as a non-cooperative game, which is proven to be a potential game. In addition, considering the deterministic deadlines of data transmissions in industrial applications, we propose a Deadline-aware Incomplete-Information-based Decentralized Resource Coordination (DIIDRC) algorithm. We also mathematically analyze that the DIIDRC algorithm converges to a collision-free optimal schedule in a resource-sufficient scenario or a nearly-optimal schedule in a resource-insufficient scenario. We conduct extensive simulations to verify the effectiveness of the DIIDRC algorithm. Evaluation results show that the DIIDRC algorithm has obvious superiorities over existing works in terms of the convergence rate and schedulable ratio.

Virtual Network Function Placement in Satellite Edge Computing With a Potential Game Approach

Satellite networks, as a supplement to terrestrial networks, can provide effective computing services for Internet of Things (IoT) users in remote areas. Due to the resource limitation of satellites, such as in computing, storage, and energy, a computation task from an IoT user can be divided into several parts and cooperatively accomplished by multiple satellites to improve the overall operational efficiency of satellite networks. Network function virtualization (NFV) is viewed as a new paradigm in allocating network resources on-demand. Satellite edge computing combined with the NFV technology is becoming an emerging topic. In this paper, we propose a potential game approach for virtual network function (VNF) placement in satellite edge computing. The VNF placement problem aims to minimize the deployment cost for each user request, furthermore, we consider that a satellite network should provide computing services for as many user requests as possible. We formulate the VNF placement problem as a potential game to maximize the overall network payoff and analyze the problem by a game-theoretical approach. We implement a decentralized resource allocation algorithm based on a potential game (PGRA) to tackle the VNF placement problem by finding a Nash equilibrium. Finally, we conduct the experiments to evaluate the performance of the proposed PGRA algorithm. The simulation results show that the proposed PGRA algorithm can effectively address the VNF placement problem in satellite edge computing.

Joint Task Offloading, D2D Pairing, and Resource Allocation in Device-Enhanced MEC: A Potential Game Approach

The emergence of intelligent applications produces the demand for computing. How to reduce the computation pressure in mobile-edge computing (MEC) under massive computation demand is an urgent problem to solve. Specifically, the allocation of heterogeneous resources, including communication resources and computing resources, needs to be optimized simultaneously. From the perspective of joint optimization of channel allocation, device-to-device (D2D) pairing, and offloading mode, this article studies the multiuser computing task offloading problem in device-enhanced MEC. The objective is maximizing the aggregate offloading benefits, i.e., the tradeoff between delay and energy consumption, of all compute-intensive users in the network. By introducing game theory, the problem is modeled as a multiuser computation task offloading game, which is proved to be an exact potential game (EPG) with at least one pure-strategy Nash equilibrium (NE) solution. In order to find a desirable solution, this article proposes a better reply-based distributed multiuser computation task offloading algorithm (BR-DMCTO). Simulation results show that the proposed offloading mechanism can improve the benefit of users, and verify the effectiveness and convergence of the proposed algorithm.

Stability of international pollution control games: A potential game approach

<p style='text-indent:20px;'>In this paper, stabilization problems for <i>n-</i>player noncooperative differential games of international pollution control (IPC) are analysed via the concept of the potential differential game (PDG) introduced by Fonseca-Morales and Hernández-Lerma (2018). By first identifying a game of IPC as a PDG, an associated optimal control problem (OCP) is obtained, whose optimal solution is a Nash equilibrium (NE) for the game of IPC. Thus, the problem of finding conditions for which the NE stabilizes the game of IPC reduces to finding conditions for which the optimal solution stabilizes the associated OCP. The concept not only yields mild conditions for saddle point stability analysed in the literature but also for the overtaking optimality of the NE of the game of IPC.</p>

Entry in Two-Sided Markets: A Potential-Game Approach

Entry in Two-Sided Markets: A Potential-Game Approach

Joint Distributed Cache and Power Control in Haptic Communications: A Potential Game Approach

Since haptic communications have an extreme requirement for the latency performance, the overdue content delivery will decrease the application experience. In order to reduce the transmission delay, we try to construct a cache-enabled D2D-assisted content-sharing framework, where the neighboring helpers preset contents and adjust their transmission power to timely serve more requesters. In particular, we modify the existing definition of the “closest” friend and take the social relationship into account to measure the overall influence caused by available neighboring helpers. However, such cooperation consumes the storage space and energy of the helpers, and accordingly, we introduce blockchain technology to propose an effective incentive mechanism where the helpers act as consensus users of blockchain and obtain the reward, i.e., the allocated computing power by the base station (BS), by contributing their local resources. Guided by this, the benefit of each helper is defined as a tradeoff of the reward and overheads. Meanwhile, we formulate the multiuser content delivery problem with the goal of maximizing the global benefits as the multiuser content delivery game. Then, we prove that the proposed game is an exact potential game (EPG) with at least one pure-strategy Nash equilibrium (NE). Meanwhile, the context-aware content delivery-based concurrent better reply (CCDCBR) algorithm is proposed to achieve a desirable solution. Finally, simulation results verify the validity of the proposed game model as well as the proposed algorithm.

Distributed Self-Optimization of Modular Production Units: A State-Based Potential Game Approach.

This article presents a novel approach for distributed optimization of production units based on potential game (PG) theory and machine learning. The core of our approach is split into two parts: the first part concentrates on the conceptual treatment of modular installed production units in terms of a PG scenario. The second part focuses on the development and incorporation of suitable learning algorithms to finally form an intelligent autonomous system. In this context, we model the production environment as a state-based PG where each actuator of each module has the role of an agent in the game aiming to maximize its utility value by learning the optimal process behavior. The benefit of the additional state information is visible in the performance of the algorithm making the environment dynamic and serving as a connector between the players. We propose a novel learning algorithm based on a global interpolation method that is applied to a laboratory scale modular bulk good system. The thorough analysis of the encouraging results yields to highly interesting insights into the learning dynamics and the process itself. The benefits of our distributed optimization approach are the plug-and-play functionality, the online capability, fast adaption to changing production requirements, and the possibility of an IEC 61131 conforming to PLC implementation.

Equilibrium refinement for a model of non-monocentric internal structures of cities: A potential game approach

Using the theory of potential games, this paper addresses the emergence of polycentric structures in cities that result from trade-offs between agglomeration economies and congestion effects. We consider a model that explains the formation of multiple business centers in cities as an equilibrium outcome under the presence of households' commuting costs and positive technological externalities between firms. We show that the model is a large-population (non-atomic) potential game. To elucidate properties of stable spatial equilibria in the model, we consider local and global maximizations of the potential function, which are known to correspond to various equilibrium refinement criteria. We find that (i) the formation of business centers (agglomeration of firms) is possible only when households' commuting costs are sufficiently low and that (ii) the size (number) of business centers increases (decreases) monotonically as communication between firms becomes easier.

Divide and Conquer in Two-Sided Markets: A Potential-Game Approach

Network effects typically generate multiple equilibria in two-sided markets. To overcome the methodological challenge of selecting an appropriate equilibrium, this paper shows that many two-sided market models are weighted potential games, and therefore a refinement of Nash equilibrium justified by many theoretical and experimental studies, potential maximization, pins down an equilibrium. Under potential maximization, platforms often subsidize one side and charge the other, i.e., divide and conquer. The primary determinant of which side to subsidize/monetize is cross-side network effects. This divide-and-conquer strategy implies that platforms are often designed to favor the money side much more than the subsidy side.

A Potential Game Approach to Distributed Operational Optimization for Microgrid Energy Management With Renewable Energy and Demand Response

In view of Internet of Energy, advanced operational optimization in microgrid energy management system (MEMS) is expected to be scalable to accommodate various participants, support plug-and-play, and optimize energy utilization. Based on potential game, this paper presents a fully distributed operational optimization for MEMS with high penetration of renewable energy and demand response. After analyzing the microgrid and potential game, the establishment and proof of an exact potential game model is presented in detail. Then the best-strategy-response iterative algorithm is proposed to find the game Nash equilibrium in a distributed way. Finally, the proposed approach is simulated in an islanded microgrid to verify its efficiency and feasibility. The significances of this paper are as follows. First, the scheme and algorithm are fully distributed. All heterogeneous individuals are treated as independent decision-making entities without a central coordinator/processor. Second, the optimization process is open and dynamic. The convergence is achieved with no limits to type or number of players, even if the process of execution is corrupted with delay or losses in communication information. Third, the consistency between individual rationality and overall importance is guaranteed, and individual best-strategy-response behaviors necessarily improve overall optimality.

A potential game approach to modelling evolution in a connected society.

When studying human behaviour, it is important to understand not just how individuals interact, but also interactions at the level of communities and populations. Most previous modelling of networks has focused on interactions between individual agents. Here we provide a modelling framework to study the evolution of behaviour in connected populations, by regarding subpopulations as the basic unit of interaction and focusing on the population-level connection structure. We find that when the underlying game played by individuals is a potential game, utilizing such a structure greatly simplifies analysis. In addition, according to known general results on the convergence of evolution dynamics to Nash equilibria in a potential game, our formulation provides a tractable model on behavioural dynamics in social networks that needs only conventional techniques from evolutionary game theory.