Solution Of Game Research Articles

ADP-Based Decentralized Load Frequency Control Schemes to Multiarea Asynchronous Markov Jumping Power Systems With Experience Replay.

This article investigates the problem of robust decentralized load frequency control (LFC) in multiarea interconnection power systems, in the presence of the external disturbances and stochastic abrupt variations, such as component failures and different load demands. To capture the different operating conditions of the load in the multiarea power systems, a Markov superposition technique is skillfully employed to model the system's component matrices. To solve the Nash equilibrium solution of the zero-sum differential game problem, an improved online adaptive dynamic programming (ADP) algorithm based on the experience replay technique (ERT) is developed, which addresses the nonlinear coupling difficulties encountered in solving the game algebraic Riccati equations (Game AREs). The proposed algorithm weakens the condition that traditional policy iteration online solutions of zero-sum games require an initial stabilizing control policy pair, and considers the fact that only the transition probability matrix is known, while the prior knowledge of the dynamics is completely unknown. The algorithm is used to obtain both a minimum LFC and maximum disturbance policy for a given disturbance rejection level. Additionally, a safer LFC controller of the power systems with the Markov jumping parameters is designed. The stability and convergence of the proposed algorithm are demonstrated. Finally, the effectiveness and good performance of the proposed design method are validated using a two-area four-mode simulation example.

Automatic construction and verification algorithm for smart contracts based on formal verification

As an emerging technology, blockchain demonstrates strong potential for applications in digital finance. As a core component of blockchain, the security and reliability of smart contracts is crucial. To ensure the high reliability of smart contracts, this study employs formal construction and verification techniques based on game theory. Initially, the profit function is defined using distortion techniques, and a game model for supply chain participation is designed. However, the equilibrium solution of the two-party game does not represent the optimal solution for the supply chain system. Therefore, the study introduces third-party participation to optimize the equilibrium solution. Finally, a probability model detection method is used to verify the constructed smart contract model. The results show that the supply chain model, analyzed through formal methods, has attributes consistent with theoretical analysis. Consequently, the research on automatic construction and verification algorithms for smart contracts based on formal verification proves to be effective and feasible in practical applications.

Real-Time Policy Optimization for UAV Swarms Based on Evolution Strategies

Multi-agent decision-making faces many challenges such as non-stationarity and sparse rewards, while the complexity and randomness of the real environment further complicate policy development. This paper addresses the high-dimensional policy optimization problems of unmanned aerial vehicle (UAV) swarms. By modeling the problem scenario as a Markov decision process, a real-time policy optimization algorithm based on evolution strategy (ES) pre-training is proposed. This approach combines decision-time planning with background planning to evaluate and integrate different sets of policy parameters in a temporal context. In the experimental phase, the policy network is trained using both ES and REINFORCE algorithms on a constructed simulation platform. Comparative experiments demonstrate the effectiveness of using ES for policy pre-training. Finally, the proposed real-time policy optimization algorithm further improves the performance of the swarm by approximately 10% in simulations, offering a feasible solution for adversarial games between swarms and extending the research scope of evolutionary algorithms.

Approximate solution of a zero-sum linear-quadratic differential game by the auxiliary parameter method: analytical/numerical study

ABSTRACT A zero-sum finite horizon linear-quadratic differential game is considered. First, the terminal-value problem for the game-theoretic Riccati matrix differential equation, associated with the considered game by the solvability conditions, is analysed. This problem may not have the solution in the entire time-interval of the game's duration. Using the method of auxiliary parameter, a sufficient condition (in the terms of the game's data) for the existence of the solution to the aforementioned terminal-value problem in the entire time-interval of the game's duration is derived. An approximate analytical solution to this problem also is obtained as a partial sum of some infinite series of functions arising in the method of auxiliary parameter. Based on this approximate solution, suboptimal state-feedback controls of the players are formally designed and justified. It is shown that the pair of these controls constitutes an approximate-saddle point in the considered game. The theoretical results of the article are illustrated by their application to approximate solution of the problem of pursuit-evasion engagement between two flying vehicles.

Employer-provided parking: Departure time choice, investment decision, and welfare effects

Employer-provided parking (EP) has become a prevalent way to reduce employees' parking delays and late arrivals through offering them free or low-price parking spaces at the workplace. This paper explores the EP effects on employees' trip scheduling, employer's EP investment decision, and commercial parking operator's pricing decision. An analytical trip scheduling equilibrium model is first presented to model the interaction between EP provision and employees' departure time choices during morning commute. A profit maximization model incorporating the employee productivity is then developed to determine the employer's optimal EP investment decision. A competitive game between employer's investment decision and commercial parking operator's parking pricing decision is analytically investigated, together with the effects of EP investment on social welfare. The results show that the EP investment can lead to a win-win situation with decreased employee commuting cost and increased firm production output; and the employer would like to provide only part of the employees with EP services. The competitive game solutions depend very much on the marginal costs of EP and commercial parking spaces. The EP investment with an excessively high commercial parking fee may hurt the society due to decreased social welfare.

Design Framework for VR Games in the Police Domain

The escalation in the number of conflicts poses various implications and consequences to society, demanding a comprehensive understanding from a law enforcement perspective. The surge in conflicts introduces significant challenges related to public safety, necessitating adaptive strategies for both conflict and crime prevention and resolution. Accordingly, effective policing must adapt to the evolving dynamics, employing a nuanced approach that addresses the root causes of conflicts and enhances community resilience to avoid or mitigate them and their broader societal impact. A pivotal role is this direction is played by the ongoing technological advancements that facilitate actions like enhancement of situational awareness, analyzing patterns and anticipating potential hotspots and activities, facts that sustain both proactive and reactive measures. In particular, technological advancements in Virtual Reality (VR) present a valuable avenue for addressing policing challenges, especially in relation to conflict resolution, mitigation, and prevention. Building VR simulations for training purposes offers a controlled yet realistic environment for officers to practice e.g., de-escalation strategies and decision-making under stress. These gaming simulations provide a platform to expose officers to diverse scenarios, fostering adaptability, encouraging communication and collaboration, and enhancing their ability to properly handle conflicts with precision and self-control. At the same time, VR systems can be developed for societal awareness and educational purposes, fostering better understanding and communication between law enforcement and the public. While a broad range of governmental, practitioner, and academic efforts exist aiming at building and incorporating VR solutions for various purposes in the police domain, specific areas that involve other societal forces and bystanders need more attention. It is then the aim of this article to capture existing design and evaluation principles as well as lessons learned from existing VR solutions in the police domain. This is done by proposing a design framework for responsible VR gaming solutions in the police domain following the Design Science Research methodology in a transdisciplinary approach.

Multicriteria Optimization of Stochastic Robust Control of the Tracking System

A multicriteria optimization of stochastic robust control with two degrees of freedom of a tracking system with anisotropic regulators has been developed to increase accuracy and reduce sensitivity to uncertain object parameters. Such objects are located on a moving base, on which sensors for angles, angular velocities and angular accelerations are installed. Improvements in the accuracy of control with two degrees of freedom include closed-loop feedback control and open-loop feedback control through the use of reference and perturbation effects. The multicriteria optimization of the stochastic robust control tracking system with two degrees of freedom with anisotropic controllers is reduced to the iterative solution of a system of four coupled Riccati equations, the Lyapunov equation, and the determination of the anisotropy norm of the system by an expression of a special form, which is numerically solved using the homotopy method, which includes vectorization matrices and iterations according to Newton's method. The objective vector of robust control is calculated in the form of a solution of a vector game, the vector gains of which are direct indicators of the quality that the system should achieve in different modes of its operation. The calculation of the vector gains of this game is related to the simulation of a synthesized system with anisotropic regulators for different modes of operation with different input signals and object parameter values. The solutions of this vector game are calculated on the basis of a set of Pareto-optimal solutions taking into account the binary relations of preferences on the basis of the metaheuristic algorithm of multi-swarm Archimedes optimization. Based on the results of the synthesis of stochastic robust control of a tracking system with two degrees of freedom with anisotropic controllers, it is shown that the use of synthesized controllers made it possible to increase the accuracy of system control, reduce the time of transient processes by 3–5 times, reduce the variance of errors by 2.7 times, and reduce the sensitivity of the system to the change of object parameters compared to typical regulators.

Zero-sum games subject to uncertain stochastic noncausal systems considering polynomial control functions

An uncertain stochastic noncausal system (USNS) is an uncertain stochastic singular system that is expected to be regular but not impulse-free. This study investigates zero-sum games (ZSGs) under the constraint of nonlinear uncertain stochastic noncausal systems (USNSs) considering polynomial control functions. Firstly, a method is introduced to convert USNSs into subsystems, including forward uncertain stochastic difference equations as well as backward uncertain stochastic difference equations. Equilibrium equations are then derived to determine the saddle-point equilibrium solution of the zero-sum game (ZSG) for USNSs. Subsequently, a unified framework is developed to find the saddle-point equilibrium and equilibrium value of the ZSG. Additionally, a numerical example and an analysis of the treatment of industrial wastewater are provided to demonstrate the effectiveness of the proposed framework.

Approaches to inclusive foreign language instruction in higher education

Introduction: The rapid development trend in innovative technological solutions significantly impacts societal activities, stimulating global digital transformation processes in the educational environment. Objectives: The article analyses innovative tools within the concept of inclusive technologies for teaching foreign languages in higher education in the digital age. Methods: Several general scientific research methods were used to achieve the research objectives, including abstraction, analysis, synthesis, induction, and deduction. Results: Attention is focused on exploring the potential of audio-lingual and audiovisual methods, didactic game solutions, case methods, reproductive methods, thematic studies, linguistic and sociocultural, and project methodologies. It has been proven that using digital educational platforms in educational programmes allows teaching foreign languages to be carried out at a high professional level, stimulating aspects of motivation and effectiveness. The study identifies that implementing innovative approaches in inclusive strategies for teaching foreign languages enables students to gain practical skills through active interaction with various non-standard methods and forms of work. Such methods include brainstorming, project work, role-playing, group work, and several others. It has been found that specific innovative approaches, such as random and cross-learning, learning through argumentation, or embodied learning, form a solid practical basis for the successful future application of acquired knowledge. Conclusions: The article proves that innovative methods of teaching foreign languages within the framework of inclusive educational technology programmes have the potential to create an effective learning environment, ensuring a significant improvement in the quality of higher education

Optimal avoidance strategy based on nonlinear approximate analytic solution of non-cooperative differential game

Abstract This study examines the pursuit-evasion game involving unmanned aerial vehicles (UAVs), with a specific focus on the scenario of N-pursuers-one-escapee. The primary objective is to develop an optimal strategy for the escapee when the pursuers possess superior capabilities. To obtain this objective, we conduct the following study. Firstly, to enhance realism, a non-cooperative differential game model is formulated, incorporating multiple motion characteristics, including aerodynamics, overloading, and imposed constraints. Secondly, the end-value performance index is subsequently converted to an integral one, simplifying the solution process of the Hamilton-Jacobi-Bellman (HJB) equation. An iterative method is utilised to determine the covariates using the Cauchy initial value problem, and its convergence and uniqueness are established. The optimal avoidance strategy is subsequently derived from the covariates. Finally, the superiority of the proposed strategy is validated through simulation experiments and compared to three advanced optimal avoidance strategies. A total of 1,000 anti-jamming simulation experiments are conducted to verify the robustness of the proposed strategy.

Применение метода ВИРТУОЗ (Вариативное Игровое Решение Трудных Учебно-Образовательных Задач) при выборе университета для обучения

In the context of the permanent transformation of the higher education system, it is not only game practices that are in demand, but also the widest possible gamification of the educational process itself in higher education. The article discusses the option of organizing the game VIRTUOZ (Variable Game Solution of Difficult Educational Problems) at an open day with applicants and their parents. The VIRTUOZ game is held in order to form an objective opinion among its participants about where to go to study on the basis of a comprehensive comparison of information from open sources on competing departments that are currently training students in a particular field in the corresponding region (regions). Variants of tasks/competitions for team members are given, allowing to compare departments by the share of employed graduates and their average salary; on the structure of academic titles of teachers; indicators of scientific activity; tuition fees. Variants of creative contests for viewers are given. In the future, it is possible to hold other competitions and introduce weights for certain aspects of the department's activities, based on their importance for applicants and their parents, either setting them in advance, or offering to do this directly to team members. Competent gamification of the educational process in Russian higher education, starting with the admission of applicants and ending with the employment of graduates, is designed to help in the development of domestic higher education today and in the long term.

The Synthesis of Optimal Control Laws Using Isaacs’ Method for the Solution of Differential Games

The Synthesis of Optimal Control Laws Using Isaacs’ Method for the Solution of Differential Games

Knowledge sharing decision-making under stochastic factors in platform ecosystems: the diversified participants' perspective

PurposeKnowledge sharing is critical to creating value in platform ecosystems. However, participants refrain from sharing knowledge and even engage in free-riding behavior, thereby causing the value co-destruction of the platform ecosystems. To encourage knowledge sharing among participants, it is essential to analyze the influencing factors and decision-making mechanisms of knowledge sharing in the platform ecosystems.Design/methodology/approachThe study investigated the issue of knowledge sharing among participants in platform ecosystems, based on the stochastic differential game model. Considering the uncertain factors, the Nash non-cooperative game, Stackelberg leader-follower game, and cooperative game models were proposed. By utilizing system dynamics and numerical simulations, the key influencing factors and mechanisms of knowledge sharing were deeply explored, consequently providing game solutions to achieve the Pareto optimality of the ecosystem.FindingsParticipants' innovation capability and the marginal benefits of knowledge-sharing positively impact knowledge-sharing decisions, while the environmental knowledge decay rate has a negative influence. The platform subsidy mode enhances the knowledge-sharing effect, and the collaborative cooperation mode can realize the Pareto optimization of the system.Practical implicationsThe research findings will provide theoretical support for fostering knowledge innovation and sustainable development of platform ecosystems. Managers should cultivate an innovative environment, establish fair reward mechanisms, and utilize subsidies to promote knowledge sharing, leading to higher value creation.Originality/valueUtilizing the stochastic differential game model, the study proposed various game-theoretic frameworks to analyze participants' knowledge-sharing strategies. The integration of system dynamics and numerical simulations provides a practical approach to understanding the key influencing factors and decision-making processes.

Updated delegated proof of stake and Nash equilibrium: A mining pool game model for blockchain network

SummaryIn the present era, blockchain technology has an extensive variety of uses in the financial, marketing, and so forth. The performance and security of the blockchain system are directly affected by the consensus algorithm. Since there are 30 consensus techniques like proof of stake, delegated proof of stake, and proof of work, and so forth in the previous years, the operating efficiency, safety, and stability still lag behind the basic requirements. To this concern, this research manuscript introduces a novel consensus mechanism‐based game model using upgraded delegated proof of stake (UDPoS) and Nash equilibrium (NE) that is UDPoS‐NE. In a blockchain network powered by UDPoS, blockchain miners provide computing power to publish blocks. Due to a single miner's limited Computing Power (CP), miners frequently join mining pools and divide the pool's profits in accordance with their respective contributions. However, certain miners launch block‐withholding attacks that lead to loss the computer power and endanger the blockchain network's effectiveness. Therefore, NE is used to calculate cooperative game solutions and optimize policy decisions of the miners to frustrate the block withholding attack by the influence of stakes and computing power on producing blocks. Using this mechanism, efficiency, and stability in the consensus process is achieved through the combined influence of computing resources and stakes on block generation. Finally, the suggested model's applicability and validity are confirmed with a throughput (600 TPS), delay (3 s), and energy consumption (<100 KWh) for 10 nodes and processing time (0.9 s) for 200 nodes.

Exploration Noise for Learning Linear-Quadratic Mean Field Games

The goal of this paper is to demonstrate that common noise may serve as an exploration noise for learning the solution of a mean field game. This concept is here exemplified through a toy linear-quadratic model, for which a suitable form of common noise has already been proven to restore existence and uniqueness. We here go one step further and prove that the same form of common noise may force the convergence of the learning algorithm called fictitious play, and this without any further potential or monotone structure. Several numerical examples are provided to support our theoretical analysis. Funding: F. Delarue acknowledges the financial support of the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme [AdG ELISA project, Grant 101054746]. A. Vasileiadis acknowledge the financial support of French ANR project ANR-19-P3IA-0002-3IA Côte d'Azur-Nice-Interdisciplinary Institute for Artificial Intelligence.

Design and Implementation of an Official History Mobile Game Solution based on 3D Technology

Design and Implementation of an Official History Mobile Game Solution based on 3D Technology

Impact of Facilitation on Cognitive Flow in a Novel Diabetes Management Rehearsal Game for Health Professions Education: Mixed Methods, Open-Label, Superiority Randomized Controlled Trial.

Though the prevalence of diabetes is set to increase, most serious game solutions typically target patient self-management and education. Few games target health care professions education, and even fewer consider the factors that may increase their efficacies. The impact of facilitation, a prominent feature of health professions education, is examined in the context of a rehearsal-based diabetes management serious game. In this mixed methods, open-label, superiority randomized controlled trial, we compare student performance, attitudes, and perceptions of a rehearsal-based diabetes management game for health care professionals. Student participants were randomized into 2 groups to play a diabetes management game. The control group played the game alone, and the intervention group played the same game alongside a facilitator tasked to moderate overall challenge levels and address queries. Both groups were administered the Flow Short Scale, a 13-item measure rated on a 7-point Likert scale ranging from 1 ("not at all") to 7 ("very much") immediately after the game. Students were then invited to voluntary focus group discussions to elicit their attitudes and perceptions of the game. Findings were subject to between-group comparisons and inductive thematic analysis respectively. A total of 48 (26 control, 22 intervention) clinical-year undergraduates from the Lee Kong Chian School of Medicine in Singapore participated in this study, with 18 continuing to the focus group discussions. Flow Short Scale results indicated the superiority of the intervention group for overall flow (t46=-2.17, P=.04) and the absorption subdomain (t46=-2.6, P=.01). Qualitative results indicated students viewed facilitation as helpful and appropriate, and were able to identify improvable elements of the game's theoretical foundations and overall design. While serious games are efficacious means of rehearsing previously learned knowledge, facilitation allows for their efficiency to be greatly increased. Such increases are likely crucial in the coming years with the increased digitization of health care professions education and the prevalence of diabetes. ClinicalTrials.gov NCT05637749; https://www.clinicaltrials.gov/study/NCT05637749.

Asymptotic behavior of Riemann solutions for the one-dimensional mean-field games in conservative form with the logarithmic coupling term

Construction of Riemann solutions for a hyperbolic system in conservative form arising from the one-dimensional mean-field games with the quadratic Hamiltonian and the logarithmic coupling term are provided in detail. Moreover, the delta shock formation is concretely analyzed from the limit of double-shock-solution as well as the emergence of vacuum state is also specifically discussed from the limit of double-rarefaction-solution when the coupling coefficient drops to zero. Accordingly, the remarkable cavitation and concentration phenomena can be closely observed and explored. Additionally, the numerical experiments are also presented in correspondence to authenticate the theoretical analysis results.

Matrix norm methods for zero-sum fuzzy matrix games with payoffs of triangular fuzzy numbers

In this paper, we mainly consider the solution of two-person zero-sum fuzzy matrix games with payoffs of triangular fuzzy numbers. Contrary to the literature, we focus on developing the methods to solve the game directly based on only the norms of the payoff matrix holistically, without solving a linear programming problem or handling sub-games created by taking the components of fuzzy numbers separately. For this purpose, we first present fuzzy versions of 1-norm and ∞-norm with the help of a ranking function and develop the fuzzy matrix norm method to obtain an approximate solution of the zero-sum fuzzy matrix game. In addition to this approach, we provide the fuzzy extended matrix norm method as an enhanced version of the method, which involves the use of newly defined fuzzy matrix norms. Thus, we managed to avoid the complexity of the optimization process of the linear programming problem via the proposed matrix norm-based methods. Finally, we illustrate the implementation of the methods by considering several benchmark examples and a 3 × 3 fuzzy matrix game.

Optimal Voltage Recovery Learning Control for Microgrids with N-Distributed Generations via Hybrid Iteration Algorithm

Considering that the nonlinearity and uncertainty of the microgrid model complicate the derivation and design of the optimal controller, an adaptive dynamic programming (ADP) algorithm is designed to solve the model-free non-zero-sum game. By combining the advantages of policy iteration and value iteration, an optimal learning control scheme based on hybrid iteration is constructed to provide stringent real power sharing for the nonlinear and coupled microgrid systems with N-distributed generations. First, using non-zero-sum differential game strategy, a novel distributed secondary voltage recovery consensus optimal control protocol is built using a hybrid iteration method to realize the voltage recovery of microgrids. Then, the data of the system state and input are gathered along a dynamic system trajectory and a data-driven optimal controller learns the game solution without microgrid physics information, enhancing convenience and efficiency in practical applications. Furthermore, the convergence analysis is given in detail, and it is proved that the control protocol can converge to the optimal solution so as to ensure the stability of the voltage recovery of the microgrid system. Convergence analysis proves the convergence of the the protocol to the optimal solution, ensuring voltage recovery stability. Simulation results validate the feasibility and effectiveness of the proposed scheme.