Potential Game Research Articles

Accelerating the Worldwide Adoption of Blockchain Technology

“To understand innovation you should ask "why not?" and not "why?" Selling (blockchain) is a problem and people and companies are blocking adoption which prevents innovation within a company, and stymies costs savings and efficientcy - that is the problem. It's a problem to be stuck in an innovation department - blockchain needs to be used in operations. Scientists and technologists need to understand one another better to create opportunity and success" - Raja Sharif, CEO, ATMPS
 In 2022, the team at ATMPS launched a potential game changer with a new approach to cell and gene therapy tracking, allowing more streamlined applications, payment options, and opportunity to realize true manufacturing safety and trust at scale, faster for personalized medicines and regimen treatments. The speaker highlights his experiences, and overcomes market challenges empasizing the need for better commercialization strategies for blockchain. In addition, the fllowing topics are addressed in the keynote:
 
 Automated Processes
 Regulatory Visibility & Compliance, FDA 21 CFR , HIPAA, GDPR
 Putting Together Blockchain Architecture for IRTS & ERH Integration
 Patient Apps and Mobile Device Issues
 Supply Chain

Bio‐inspired Design of Bidirectional Oxygen Reduction and Oxygen Evolution Reaction Molecular Electrocatalysts

AbstractThe proper utilization of renewable energy sources has emerged as a major challenge in our pursuit of a sustainable and carbon‐neutral energy landscape. Small molecule activation is a key component for proper utilization of renewable energy resources, where O2/H2O redox couple is reckoned to be a potential game changer. In this regard, electrocatalytic oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) have become the prime interest of catalyst designers. Typically, these ORR and OER electrocatalysts are developed distinctly; however, very soon, the requirement of a bidirectional ORR/OER electrocatalyst becomes obvious for practical applicability and rapid energy transduction purposes. A bidirectional catalyst is defined as a catalyst capable of driving a redox reaction in opposing directions. This review has portrayed the development of enzyme structure‐inspired design of molecular bidirectional ORR/OER catalysts. The strategic incorporation of secondary and outer coordination sphere features has significantly enhanced the performance of these catalysts, which can be monitored via the key catalytic parameters. These bifunctional OER/ORR catalysts are vital for metal‐air battery and fuel cell applications and appropriately poised to lay the foundation for an efficient, economical, and eco‐friendly pathway for sustainable energy usage with the rational assembly of energy converting and storage devices.

Privacy‐preserving model splitting and quality‐aware device association for federated edge learning

AbstractFederated edge learning (FEEL) provides a promising device‐edge collaborative learning paradigm, which enables edge devices to parallel participate in model co‐creation while preserving user privacy, opening countless opportunities to enable edge intelligence. With the growing demand for intelligent services, extensive FEEL deployment is inevitable. Nevertheless, existing FL schemes neglect two unique features (i.e., resource heterogeneity and data heterogeneity) in real‐world edge learning and thus may negatively affect the training efficiency and accuracy. Specifically, (1) heterogeneous and limited device resources cause massive laggards, which bring intolerable training delay; (2) heterogeneous data distribution causes device quality divergence, bringing severe training accuracy degradation. This article proposes a split‐based FEEL framework and an adaptive model splitting and quality‐aware device association scheme (MSDA) to tackle the aforementioned challenges. MSDA contains two levels: at the model splitting level, according to device capability and model structure, an adaptive splitting mechanism is proposed to provide a low‐latency and privacy‐preserving model splitting strategy for each device and guide subsequent device association. At the device association level, each device is simulated as a player with a quality weight in the potential game. Then a quality‐aware decentralized device association mechanism is designed to ensure that more high‐quality devices upload local updates before the deadline with the help of the edge server. Finally, experimental results demonstrate that MSDA yields significant improvements, achieving up to 3.1 training speedup and 39% accuracy improvement compared to state‐of‐the‐art methods.

Categorisation of cats: Managing boundary felids in Aotearoa New Zealand and Britain

Abstract Management of domestic and wild animals is an integral part of conservation and is often based on how an animal is categorised. For example, feral cats are often killed, while valued companion cats and native wildcats are protected. Drawing on qualitative research and using the concept of boundary‐work, this paper examines the complex categorisation and management of cats within conservation in Britain and Aotearoa, New Zealand (NZ). We examine how, both in theory and in practice, valued companion and wildcats are distinguished from unprotected feral cats, and in‐between categories of stray and hybrid cats. We demonstrate that stakeholders draw boundaries between cat categories differently. These differences in boundary‐drawing reflect the inherent blurriness of category boundaries, practical challenges and, importantly, differences in values, in particular whether priority is placed on the life of the cat or the cat's potential victim, particularly native or game birds. This can mean that laws outlining protections for specific categories of animals have limited effect if, in practice, those encountering cats draw boundaries differently. This paper also reports on important differences between the two case studies. In NZ, even cat advocates support the humane killing of unambiguously feral cats while this is less true in Britain. Furthermore, due to the nature of the contexts, conservationists in NZ are more inclined to assume that ambiguous cats are feral whereas conservationists in Britain are more inclined to assume that they are wildcats. This paper demonstrates that values not only shape people's perceptions and treatment of animals but also how they draw boundaries between them. This finding may have important implications for understanding other controversies in conservation and animal management. Read the free Plain Language Summary for this article on the Journal blog.

Game-theoretical approach for task allocation problems with constraints

The distributed task allocation problem, as one of the most interesting distributed optimization challenges, has received considerable research attention recently. Previous works mainly focused on the task allocation problem in a population of individuals, where there are no constraints for affording task amounts. The latter condition, however, cannot always be hold. In this paper, we study the task allocation problem with constraints of task allocation in a game-theoretical framework. We assume that each individual can afford different amounts of task and the cost function is convex. To investigate the problem in the framework of population games, we construct a potential game and calculate the fitness function for each individual. We prove that when the Nash equilibrium point in the potential game is in the feasible solutions for the limited task allocation problem, the Nash equilibrium point is the unique globally optimal solution. Otherwise, we also derive analytically the unique globally optimal solution. In addition, in order to confirm our theoretical results, we consider the exponential and quadratic forms of cost function for each agent. Two algorithms with the mentioned representative cost functions are proposed to numerically seek the optimal solution to the limited task problems. We further perform Monte Carlo simulations which provide agreeing results with our analytical calculations.

Potential Game-Based Decision-Making for Autonomous Driving

Decision-making for autonomous driving is challenging, considering the complex interactions among multiple traffic agents (including autonomous vehicles (AVs), human-driven vehicles, and pedestrians) and the computational load needed to evaluate these interactions. This paper develops two general potential game-based frameworks, namely, finite and continuous potential games, for decision-making in autonomous driving. The two frameworks account for the AVs’ two types of action spaces, i.e., finite and continuous action spaces, respectively. The developed frameworks provide theoretical guarantees for the existence of pure-strategy Nash equilibria and for the convergence of the Nash equilibrium (NE) seeking algorithms. The scalability challenge is also addressed. In addition, we provide cost function shaping approaches such that the agents’ cost functions not only reflect common driving objectives but also yield potential games. The performance of the developed algorithms is demonstrated in diverse traffic scenarios, including intersection-crossing and lane-changing scenarios. Statistical comparative studies, including 1) finite potential game vs. continuous potential game, 2) best response dynamics vs. potential function optimization, and 3) potential game vs. reinforcement learning (RL) vs. control barrier function (CBF), are conducted to compare the robustness against various surrounding vehicles’ strategies and to compare the computational efficiency. It is shown that the developed potential game frameworks have better robustness than RL and than CBF if the surrounding vehicles are not safety-conscious, and are computationally feasible for real-time implementation.

Consensus of Hybrid Behavior for Graphical Coordination Games

This brief is concerned with consensus of hybrid behavior for graphical coordination games. We take into account a multi-agent system with two agent-unions that have various preferences. Interactions among agents in the multi-agent system are modelled as a hybrid graphical coordination game, in which three kinds of payoff matrices are designed. By using potential game theory, necessary and sufficient conditions are established for achieving consensus of the hybrid graphical coordination game. Finally, several numerical simulations are provided to validate the effectiveness of our theoretical results.

High order computation of optimal transport, mean field planning, and potential mean field games

Mean-field games (MFGs) have shown strong modeling capabilities for large systems in various fields, driving growth in computational methods for mean-field game problems. However, high order methods have not been thoroughly investigated. In this work, we explore applying general high-order numerical schemes with finite element methods in the space-time domain for computing the optimal transport (OT), mean-field planning (MFP), and potential MFG problems. We conduct several experiments to validate the convergence rate of the high order method numerically. Those numerical experiments also demonstrate the efficiency and effectiveness of our approach.

QoS driven cost-efficient resource allocation in edge computing: A distributed game theoretic approach

In edge computing, app vendors provide task-offloading services to small intelligent devices with limited resources via edge servers. Users of these devices aim to utilize a better quality of service at a lower cost, and the app vendor tries to maximize their user base. In this paper, we propose an edge computing resource allocation technique to address the critical issue of maintaining a trade-off between user cost and QoS while increasing the number of users served by the app vendor. In the proposed technique, we formulate the problem as an Edge Server Selection Game (ESSGame), where users can optimize individual objective functions. An Edge Server Selection Algorithm (ESSA) is presented by which the system quickly converges to a pure Nash equilibrium (PNE). It is proved that ESSAGame is a potential game with at least one PNE. The quality of the PNE in ESSGame is also determined in terms of the Price of Stability (PoS). Numerical results are also presented for the performance of the proposed technique.

Two-Hop Cooperative Caching and UAVs Deployment Based on Potential Game

This paper explores the joint cache placement and 3D deployment of Unmanned Aerial Vehicle (UAV) groups, utilizing potential game theory and a two-hop UAV cooperative caching mechanism, which could create a tradeoff between latency and coverage. The proposed scheme consists of three parts: first, the initial 2D location of UAV groups is determined through K-means, with the optimal altitude based on the UAV coverage radius. Second, to balance the transmission delay and coverage, the MOS (Mean Opinion Score) and coverage are designed to evaluate the performance of UAV-assisted networks. Then, the potential game is modeled, which transfers the optimization problem into the maximization of the whole network utility. The locally coupling effect resulting from action changes among UAVs is considered in the design of the potential game utility function. Moreover, a log-linear learning scheme is applied to solve the problem. Finally, the simulation results verify the superiority of the proposed scheme in terms of the achievable transmission delay and coverage performance compared with two other tested schemes. The coverage ratio is close to 100% when the UAV number is 25, and the user number is 150; in addition, this game outperforms the benchmarks when it comes to maximizing MOS of users.

Joint optimized multi-user access and UAV deployments based on heterogeneous revenue in IoT network

With the explosion demands of computation in the internet-of-things (IoT) sector, as an emerging technique with considerable computational capability, unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) has been proposed to reduce the latency and improve the quality-of-experience (QoE) of the user equipments (UEs). However, being existed massive heterogeneous UEs with different time sensitivity, how to offload the tasks of UEs, and where to deploy the positions of UAVs are two critical factors for the MEC system. In this work, we investigate a multi-UAV-assisted MEC system to maximize the sum of UEs’ revenue in the event that natural disasters block communication conditions. A two-hierarchy Stackelberg game framework model is constructed, with the upper-layer UAVs as leaders performing location deployments, while the lower-layer UEs as followers conducting offloading selections. Different from the existing revenue functions, for followers, we adopt three revenue functions in terms of different UEs having distinct responses to time consuming. Furthermore, both the leader-layer and follower-layer subgame are proved to be exact potential games (EPG) with at least one Nash equilibrium (NE), then demonstrating the existence of Stackelberg equilibrium (SE). Additionally, we evaluate the price of anarchy (PoA) of the NE and illustrate that the value of PoA is relatively small, which corroborates that our solution is close to the global optimum. Finally, the simulation results demonstrate that the proposed solution can improve the total revenue of UEs under guaranteeing the convergence.

Game-Combined Multi-Agent DRL for Tasks Offloading in Wireless Powered MEC Networks

Wireless powered mobile edge computing (MEC) networks, where wireless devices (WDs) are allowed to offload parts of computation-intensive tasks to remote servers and charge the built-in batteries over the air, have been envisaged as a promising technology to ensure the ultra-low-power requirement and enhance the continuous work capacity of WDs. However, when multiple WDs coexist in the network, it is non-trivial to minimize the total tasks delay since the optimization variables are intrinsically coupled. Even more, channels are dynamically varying from time to time and the tasks are unpredictable, which aggravates the difficulty to obtain the closed-form solution. Although reinforcement learning (RL) has been proved to be effective for such complex optimization problems, there is still the challenge that the training of neural networks is time-consuming. This paper considers a challenging hybrid tasks offloading scenario, where offloading tasks can be partially executed locally and remotely in parallel, and each WD is endowed to take both the active RF-transmission and passive backscatter communication (BackCom) for remote tasks offloading. Furthermore, a game-combined multi-agent deep deterministic policy gradient (MADDPG) algorithm is proposed to minimize the total tasks delay with the fairness consideration of multiple WDs, i.e., potential game for offloading decision and MADDPG for time scheduling and harvested energy splitting. The introduction of potential game which can be proved to converge with finite iterations, helps to accelerate the training and reduce the computation complexity. Equipped with the feature of ‘centralized training with decentralized execution,’ once well trained, each agent in MADDPG can figure out the proper time scheduling and harvested energy splitting independently without sharing information with others. Besides the unilateral contention among WDs for the offloading decision by potential game, a fully decentralized framework is finally designed for the proposed algorithm. Numerical results demonstrate that the game-combined MADDPG algorithm can achieve the near-optimal performance compared with existing centralized approaches, and reduce the convergence time compared with other no-game learning approaches.

Noncooperative and Cooperative Urban Intelligent Systems: Joint Logistic and Charging Incentive Mechanisms

Autonomous vehicles (AVs) have become an emerging crucial component of the intelligent transportation system (ITS) in modern smart cities. In particular, coordinated operations of AVs can potentially enhance the quality of public services, e.g. logistic and AV charging services. However, the joint logistic and AV charging scenario involves the sophisticated interactions between a large number of complicated agents, dynamic logistic, and electricity prices in real-world systems. Since AVs are individuals owned by different parties, the design of attractive incentive to motivate them to provide multiple public services becomes a fundamental issue. In this paper, we develop an urban intelligent system (UIS) by exploiting the efficient incentive mechanisms, e.g. non-cooperative and cooperative game theoretic approaches, to motivate the AVs to provide logistic and charging services in UIS. For the non-cooperative game approach, we formulate the interaction between the selfish AVs and the aggregator as a Stackelberg game. Meanwhile, the aggregator, known as the leader in the game, aims to decide the logistic and electricity trading prices, and then the AVs, executed as the followers, determine their service schedules. Furthermore, considering that all the players are willing to cooperate, we develop a cooperative potential game for the selfless AVs to maximize the social welfare of the UIS. These case studies demonstrate the effectiveness and practicability of proposed incentive mechanisms that can motivate EVs to provide high quality logistic and charging services by maximizing their utilities. Also, both the proposed schemes provide significant system revenues than that of conventional system optimization-based approaches.

A Double Auction Mechanism for Task Scheduling of An EOS Constellation

This study investigates the task scheduling problem for an earth observation satellite (EOS) constellation. The EOS constellation needs to collaboratively complete a set of observation tasks, but each EOS should satisfy the observation order specification described by a task graph due to its motion constraints. The goal is to maximize the total number of observation tasks by the constellation, for which repeated observations are counted only for once. Firstly, the task scheduling problem is formulated as an exact potential game and the goal of the task scheduling problem is converted to find a Nash equilibrium (NE). Secondly, a double auction strategy is proposed to solve for an NE iteratively and a sieving scheme is presented for the buyer to build its candidate set. It is proved that our algorithm converges to an NE in finite steps.

Robustness of dynamics in games: A contraction mapping decomposition approach

A systematic framework for analyzing dynamical attributes of games has not been well-studied except for the special class of potential or near-potential games. In particular, the existing results have shortcomings in determining the asymptotic behavior of a given dynamics in a designated game. Although there is a large body of literature on developing convergent dynamics to the Nash equilibrium (NE) of a game, in general, the asymptotic behavior of an underlying dynamics may not be even close to a NE. In this paper, we initiate a new direction toward game dynamics by studying the contraction properties of the map of dynamics in games. To this aim, we first decompose the map of a given dynamics into contractive and non-contractive parts and then explore the asymptotic behavior of those dynamics using the proximity of such decomposition to contraction mappings. In particular, we analyze the non-contractive behavior for better/best response dynamics in discrete-action space sequential/repeated games and show that the non-contractive part of those dynamics is well-behaved in a certain sense. That allows us to estimate the asymptotic behavior of such dynamics using a neighborhood around the fixed point of their contractive part proxy. Finally, we demonstrate the practicality of our framework via an example from the duopoly Cournot games.

Best-response dynamics, playing sequences, and convergence to equilibrium in random games

We analyze the performance of the best-response dynamic across all normal-form games using a random games approach. The playing sequence—the order in which players update their actions—is essentially irrelevant in determining whether the dynamic converges to a Nash equilibrium in certain classes of games (e.g. in potential games) but, when evaluated across all possible games, convergence to equilibrium depends on the playing sequence in an extreme way. Our main asymptotic result shows that the best-response dynamic converges to a pure Nash equilibrium in a vanishingly small fraction of all (large) games when players take turns according to a fixed cyclic order. By contrast, when the playing sequence is random, the dynamic converges to a pure Nash equilibrium if one exists in almost all (large) games.

Perspectives on the strategic importance of digitalization for Modernizing African Agriculture

Africa's agriculture has over time, improved and expanded in scale and efficiency, yet its productivity is far from its optimum, and the majority of the continent is still food insecure. To harness its potential for a drastic increase to avoid continued mass hunger and support the bioeconomy’s growth, farm productivity in Africa must be retooled. This paper examines the role of digitalization as a potential game changer and an impetus for a transformational future for large-scale and smallholder farming and livelihoods through driving technological and methodological innovation and automation. Digitalization is seen here as consisting of four supporting elements: sensors (in situ or remote), wireless transmission of data, artificial intelligence (AI), and transmission of diagnoses and or recommendations to the end user. In addition to strengthening food security on the continent and globally and the ensuing economic benefits, other cross-cutting effects are presented. These include risk management through early warning systems for natural disasters, improved decision-making with big data, reduction in rural-urban migration by the attraction of youth towards agriculture, and the improvement in environmental stewardship and building climate resilience. Challenges to realizing the benefits of digitalization, including lack of infrastructure and human resource expertise and the role of public-private partnerships in developing solutions, are also addressed.

Exploring potential applications of measles and rubella microarray patches (MR-MAPs): use case identification.

Innovative vaccine products will be critical in helping to address the existing implementation barriers that have prevented the achievement of the measles and rubella (MR) vaccine coverage targets. Overcoming those barriers will be necessary to achieve the "Immunization Agenda 2030" goals. Microarray patches (MAPs), an innovative needle-free delivery device currently in clinical development, can be a potential game changer in this respect and contribute to the equitable delivery of vaccines in low- and middle-income countries and pandemic preparedness and response. Developing in-depth knowledge of the most desired and impactful uses of MRMAPs can prove critical to identifying the critical attributes of the target product profile, informing policy and adoption decisions, and helping to evaluate the potential public health and economic value of this technology. The first step in this process is the definition of the potential use cases for MR-MAPs, i.e., where and how this product is most likely to be used within the immunization programme. By applying a design-based user-centric approach, we implemented a three-step process, including a desk review, a survey, and interviews, to define the most relevant use cases for MR MAPS. Six use cases have been identified as relevant across all different countries and immunization programme designs and validated by experts. The identified use cases have already informed the demand estimate for MR-MAPs and provided the foundation for developing an initial full vaccine value assessment. We believe that, in the future, they will be highly valuable in ensuring that the roll-out of this promising innovation is designed in a way that maximizes the impact, particularly in populations and countries that are most in need.

User-Oriented Edge Node Grouping in Mobile Edge Computing

In mobile edge computing networks, densely deployed access points are empowered with computation and storage capacities. This brings benefits of enlarged edge capacity, ultra-low latency, and reduced backhaul congestion. This paper concerns edge node grouping in mobile edge computing, where multiple edge nodes serve one end user cooperatively to enhance user experience. Most existing studies focus on centralized schemes that have to collect global information and thus induce high overhead. Although some recent studies propose efficient decentralized schemes, most of them did not consider the system uncertainty from both the wireless environment and other users. To tackle the aforementioned problems, we first formulate the edge node grouping problem as a game that is proved to be an exact potential game with a unique Nash equilibrium. Then, we propose a novel decentralized learning-based edge node grouping algorithm, which guides users to make decisions by learning from historical feedback. Furthermore, we investigate two extended scenarios by generalizing our computation model and communication model, respectively. We further prove that our algorithms converge to the Nash equilibrium with upper-bounded learning loss. Simulation results show that our mechanisms can achieve up to 96.99% of the oracle benchmark.

Game-Based Task Offloading and Resource Allocation for Vehicular Edge Computing With Edge-Edge Cooperation

Vehicular Edge Computing (VEC) enables task offloading from vehicles to the edge servers deployed on Road Side Units (RSUs), thus enhancing the task processing performance of the vehicles. However, in a multi-RSU VEC scenario, the uneven geographical distribution of the vehicles naturally causes the load imbalance among the edge servers and leads to the overload and performance degradation problems of the edge servers in hot areas. To this end, in this paper, we propose a joint task offloading and resource allocation for VEC with edge-edge cooperation, in which the tasks offloaded to a high-load edge server can be further offloaded to the other low-load edge servers. Our objective is to minimize the total task processing delay of all the vehicles while guaranteeing the task processing delay tolerance and the holding time of each vehicle. An M/M/1 queue is used to model the task queuing and task computing processes on each RSU. An exact potential game is adopted to model the competition process for the task offloading among the RSUs. A two-stage iterative algorithm is designed to decompose the optimization problem into two stages and solve them iteratively. We analyze the computational complexity of the algorithm and conduct extensive simulations by varying different crucial parameters. The superiority of our scheme is demonstrated in comparison with 3 other reference schemes.