Game Strategy Research Articles

Decentralized Control for Large-Scale Systems With Actuator Faults and External Disturbances: A Data-Driven Method.

This article investigates optimal control for a class of large-scale systems using a data-driven method. The existing control methods for large-scale systems in this context separately consider disturbances, actuator faults, and uncertainties. In this article, we build on such methods by proposing an architecture that accommodates simultaneous consideration of all of these effects, and an optimization index is designed for the control problem. This diversifies the class of large-scale systems amenable to optimal control. We first establish a min-max optimization index based on the zero-sum differential game theory. Then, by integrating all the Nash equilibrium solutions of the isolated subsystems, the decentralized zero-sum differential game strategy is obtained to stabilize the large-scale system. Meanwhile, by designing adaptive parameters, the impact of actuator failure on the system performance is eliminated. Afterward, an adaptive dynamic programming (ADP) method is utilized to learn the solution of the Hamilton-Jacobi-Isaac (HJI) equation, which does not need the prior knowledge of system dynamics. A rigorous stability analysis shows that the proposed controller asymptotically stabilizes the large-scale system. Finally, a multipower system example is adopted to illustrate the effectiveness of the proposed protocols.

Enhancing deep reinforcement learning for scale flexibility in real-time strategy games

Real-time strategy (RTS) games present a unique challenge for AI agents due to the combination of several fundamental AI problems. While Deep Reinforcement Learning (DRL) has shown promise in the development of autonomous agents for the genre, existing architectures often struggle with games featuring maps of varying dimensions. This limitation hinders the agent’s ability to generalize its learned strategies across different scenarios. This paper proposes a novel approach that overcomes this problem by incorporating Spatial Pyramid Pooling (SPP) within a DRL framework. We leverage the GridNet architecture’s encoder–decoder structure and integrate an SPP layer into the critic network of the Proximal Policy Optimization (PPO) algorithm. This SPP layer dynamically generates a standardized representation of the game state, regardless of the initial observation size. This allows the agent to effectively adapt its decision-making process to any map configuration. Our evaluations demonstrate that the proposed method significantly enhances the model’s flexibility and efficiency in training agents for various RTS game scenarios, albeit with some discernible limitations when applied to very small maps. This approach paves the way for more robust and adaptable AI agents capable of excelling in sequential decision problems with variable-size observations.

Performance Assessment through Work Gamification: Investigating Engagement

Purpose: The purpose of this study is to evaluate, investigate and assess the impact of gamification on the performance of online transportation drivers via social values, motivation and participatory engagement.Design/methodology/approach: This quantitative study is based on primary data from 110 online transportation drivers in five cities in the Central Java Province region: Semarang, Pekalongan, Kudus, Purwokerto and Solo. Partial least squares (PLS) method is used to analyse and evaluate data.Findings/results: The research results show that job gamification positively and significantly influences driver performance through social value, motivation and participatory engagement.Practical implications: The findings can be applied to increase employee performance in a business or organisation that supports a sustainable, friendly company. They also offer practical basics to make decisions in increased employee engagement, enhanced productivity, improved learning and skill development, social value and collaboration.Originality/value: While the study establishes a positive relationship between gamification and driver performance through the mediating factors of social value, motivation and participatory engagement, future research could delve deeper into understanding the specific gamification techniques. Those design elements are also the most effective in the context of online transportation. Additionally, exploring potential moderating factors, such as the demographics of drivers or market conditions, could provide a more nuanced understanding of the gamification-performance relationship. This in-depth exploration could help transportation companies tailor their gamification strategies for maximum impact and address potential limitations in current research.

An Efficient Application of Modified YOLOv5 in Basketball Player Detection and Analysis

Effective analysis helps players evaluate their performance, make necessary adjustments, and develop diverse game strategies. Moreover, the analysis provides viewers with different perspectives, enhancing their understanding of the game. This study aims to develop a basketball player detection and analysis system to assist in analyzing on-court situations. The system uses perspective transformation to obtain player tracking information on the top view image in basketball games. The system uses a modified you only look once (YOLO) v5 model that replaces the backbone of YOLOv5s with the MobileNetv3-small architecture for player detection. Compared to the original YOLOv5, the modified YOLOv5 reduces parameters from 7.02 × 106 to 3.5 × 106, a decrease of 49.8%. The number of frames obtainable per second increases from 12.4 to 17.5, an improvement of about 41.1%. Finally, the system performs perspective transformation and tracks the detected player positions onto the top-view court image using the YOLOv5 model.

Analytical modeling of cylindrical eddy current brakes and multi-objective optimization based on game theory

With the research and development of eddy current brake (ECB), today ECBs have been applied in a variety of fields including vibration control and braking of strong impact loads.The application of a new cylindrical structure eddy current brake (ECB) for strong impact braking of large machinery has been discussed recently. Its high-speed and high-kinetic-energy braking conditions require different analytical and optimization design models and methods from what has been addressed in previous studies. For subsequent more engineering-oriented research and optimization, modeling methods are needed, as well as analysis and optimization studies for braking forces and critical speeds of interest. In this work, a magnetic equivalent circuit model is established, and the influence of eddy current induced during application is taken into account by considering it as a magnetomotive force in the model. The braking force is calculated with the MEC model and an approximate electric field cross-section method. A small prototype experiment is carried out and proves the correctness of the proposed model. Using the proposed and FEM model, parameters of the ECB are analyzed. Then, aiming at design objectives of the ECB that are somewhat competitive under this special braking condition, a multi-objective optimization model is established using the Stackelberg game strategy. An FEM model is built and assessed based on optimization results. The results indicate that the multi-objective optimization model based on the Stackelberg game is effective for the design of this ECB structure.

Research on interregional oil cooperation-sanctions with evolutionary game

PurposeThe game strategies differ when different regions participate in the oil game. Under what circumstances will different participants choose cooperation or sanction strategies? This is the core issue of this paper.Design/methodology/approachRegarding the current and future game behavior between different regions in the oil trade, this paper constructs an evolutionary game model between two regions to explore the possibility of sanctions strategies between the two sides in different situations.FindingsThe research finds: (1) When the benefits of in-depth cooperation between the two regions are greater, both sides tend to adopt cooperative strategies. (2) When the trade conflict losses between the two regions are smaller, both sides adopt sanctions strategies. (3) When a strong region trades with a weak region, if the former adopts a sanctions strategy, the net profits are greater than the benefits of in-depth cooperation between the two regions. If the latter adopts a sanctions strategy, the net profits are less than the trade conflict losses between the two regions. There will be the strong region adopting a sanctions strategy and the weak region adopting a non-sanctions strategy. At this time, the latter should reasonably balance the immediate and future interests and give up some current interests in exchange for in-depth cooperation between the two regions. Otherwise, it will fall into the situation of unilateral sanctions by the strong against the weak.Originality/valueThere is no paper in the existing literature that uses the evolutionary game method to analyze the oil game problem between the two regions. This paper constructs a two-party evolutionary game model composed of crude oil importers and crude oil exporters and, based on this, analyzes the evolutionary stability between the two regions under sanctions and cooperation strategies, which enriches the energy research field.

Resilient pursuit evasion guidance with feedback game strategy

This paper proposed a resilient guidance strategy for planar pursuit evasion, in which the homing guidance problem is formulated as a Stackelberg game. As is known, the most salient challenge for pursuit evasion guidance is how to ensure an interception despite both players (i.e., missile and target) actively optimize their own interest with preferably incomplete information. Toward this, we introduce a game-theoretic guidance strategy that effectively integrates incentive feedback strategy into pursuit evasion game. In particular, the proposed resilient guidance law takes the form of Stackelberg game conjecture with missile/interceptor as the designated leader, and rigorously proves that leader's interest can be best served with a properly designed feedback gain, and an interception can be made possible in spite of incomplete knowledge on target's intentions (i.e., performance index). Simulation results verify the performance of the proposed strategy.

Gamification strategy to promote social and human factors in the training of software engineers: A case study

Gamification strategy to promote social and human factors in the training of software engineers: A case study

The role of training evaluation as a mediator in the adoption of the gamification methodology in Indian business

ABSTRACT The objective of this study is to examine the extent to which Indian firms have implemented gamification methodology for training purposes and to evaluate the impact of training assessment on the adoption and effectiveness of gamification strategies. This research used survey approach to analyze its hypothesis and goal. 260 surveys were valid. This study used Smart-PLS 4 to analyze data. Gamification, evaluation, and transfer were explored in this study. Gamification, training evaluation, and transfer were explored in this study. Findings suggested that the gamification training technique and training transfer make employee training evaluation crucial to the training process. Gamification in training program and employee training assessment aid training transfer. This research examines the profound impact that gamified training may have on improving organizational learning. The research, conducted with a rigorous methodology, demonstrates a clear and beneficial relationship between gamification, training transfer, and the mediator training evaluation. This highlights the significance of implementing comprehensive assessment methods. The results emphasize the need of incorporating gamification into training methods to enhance the transfer of skills and improve overall organizational performance. Managers and department heads embrace gamification as a training method and training assessment systems in organisations since evaluation is key to training success.

Trilateral Evolutionary Game Strategy for the Design Optimization of Engineering General Contracting Projects in the Chinese Context

The engineering general contracting mode is an advanced engineering transaction mode, and design optimization is one of the important driving forces for vigorously promoting the general contracting mode. The application proportion of the general contracting mode in infrastructure projects is not high, the number of successful projects is not large, and the implementation effect is not ideal. One of the main reasons is that the design optimization theory and practice of international standard general contracting projects cannot adapt to the general contracting projects in the Chinese context, making local general contracting projects face huge challenges such as low enthusiasm for design optimization from all parties and unsatisfactory design optimization effects. Therefore, under the premise of bounded rationality, when the owner adopts control methods of different intensities, an evolutionary game study on the selection of design optimization strategies between the design and construction parties is carried out, and stability control strategies are proposed through case experiments and simulations. The research results indicate the following: firstly, the design optimization of general contracting projects in the Chinese context is feasible, but it depends on the distribution ratio of benefits obtained from the design optimization. Compared with general civil construction general contracting projects, the design optimization allocation ratio of industrial construction general contracting projects is more significant; secondly, the mixed control method of strong control and weak control is the optimal choice for the owner of the general contracting project; and thirdly, there are multiple evolutionary stable points, and mechanism design or incentive measures should be used to guide owners to choose weak control strategies, while design and construction parties should choose their design optimization strategies. The research results provide a reference for owners to determine the proportion and scheme of design optimization allocation, and for construction parties to determine design optimization strategies.

The Impact of a Digital Game-Based AI Chatbot on Students’ Academic Performance, Higher-Order Thinking, and Behavioral Patterns in an Information Technology Curriculum

In the age of intelligence, information technology (IT) education has become the focus of attention in the education sector. However, traditional teaching methods fall short in motivating students and fostering higher-order thinking and have difficulty providing a personalized learning experience. Although AI chatbots can provide instant feedback as an innovative teaching tool, it is still challenging to fully enhance learner engagement. Based on this, this study developed a digital game-based AI chatbot system to enhance students’ learning experience through digital game-based learning strategies. This study utilized a quasi-experimental design with the experimental group using a digital game-based AI chatbot and the control group using a traditional AI chatbot. A comparison was made between the two groups concerning student learning performance in IT courses, higher-order thinking (including problem-solving, computational thinking, and creativity), learning motivation, and flow experience. In addition, the behavioral patterns of high-achieving and low-achieving students in the experimental group were analyzed. The results showed that the experimental group was significantly better than the control group in academic performance, problem-solving, computational thinking, learning motivation, and flow experience, but there was no significant difference in creativity tendency. Behavioral pattern analysis showed that high-achieving students in the experimental group showed more systematic learning strategies, while low-achieving students relied more on immediate feedback and external help, but both high- and low-achieving groups were able to actively talk to the AI chatbot and actively explore problem-solving strategies in the digital game. Therefore, AI chatbots based on digital games can be effectively used in IT courses to help students construct knowledge and develop higher-order thinking.

Addressing Toxicity and Extremism in Games: Conversations with the Video Game Industry

There has been an increased focus on reducing hate, harassment, and extremist exploitation in digital games through solutions from community management, trust and safety, and design perspectives. Despite this attention, the industry lacks basic standards around these issues and efforts in this area are stymied by the ubiquity of “toxic gamer culture” and fears in the industry about discussing extremism and hate within games. Today, a patchwork of strategies and tools exist. In this study, focus groups with industry professionals were conducted to increase understanding of industry professional's perceptions of these problems and solutions. Several primary themes regarding solutions emerged: game and social system design strategies, moderation and player report strategies, and wish list solutions.

Optimal strategies in fractional games: vertex cover and domination

Optimal strategies in fractional games: vertex cover and domination

Optimal bidding strategy for the price-maker virtual power plant in the day-ahead market based on multi-agent twin delayed deep deterministic policy gradient algorithm

In recent years, distributed energy resources (DERs) have developed greatly, and the total capacity of resources aggregated by virtual power plants (VPPs) has increased. The role VPP plays in the market is changing. It is necessary to explore VPP's bidding strategy in the electricity market based on the impact of VPP on the clearing price. At the same time, the strategy game of different market players will also impact VPP's strategy. The multi-agent twin delayed deep deterministic policy gradient (MATD3) algorithm is used to solve the problem of price-maker VPP participation in the day-ahead (DA) market. VPP is required to submit the power and prices of electricity in multi-segments at different times. In the market bidding process, the main market participants are considered as agents. Iterative refinement of each player's strategy is achieved through multi-agent reinforcement learning, with the primary aim of maximizing revenue. For the VPP, the MATD3 algorithm improved the reward by approximately 65 % and increased the convergence speed by 17 % compared to the multi-agent deep deterministic policy gradient and multi-agent proximal policy optimization algorithms. The effects of different influences on VPP's bidding strategy are analyzed, which can provide a decision-making reference for market participants.

Adaptive strategy optimization in game-theoretic paradigm using reinforcement learning

Parrondo's paradox refers to the counterintuitive phenomenon whereby two losing strategies, when alternated in a certain manner, can result in a winning outcome. Understanding the optimal sequence in Parrondo's games is of significant importance for maximizing profits in various contexts. However, the current predefined sequences may not adapt well to changing environments, limiting their potential for achieving the best performance. We posit that the optimal strategy that determines which game to play should be learnable through experience. In this Letter, we propose an efficient and robust approach that leverages Q learning to adaptively learn the optimal sequence in Parrondo's games. Through extensive simulations of coin-tossing games, we demonstrate that the learned switching strategy in Parrondo's games outperforms other predefined sequences in terms of profit. Furthermore, the experimental results show that our proposed method can be easily adjusted to adapt to different cases of capital-dependent games and history-dependent games. Published by the American Physical Society 2024

Gamified-flipped English classes and L2 learners’ achievement and perceptions

ABSTRACT This study aimed to investigate the benefits of incorporating gamification within a flipped English classroom to evaluate the resulting synergies on language learners’ overall L2 achievement (grammar, vocabulary, and reading comprehension) and their perceptions of this didactic approach. A total of 327 lower-intermediate L2 learners were assigned into three groups: gamified-flipped class (GFC), flipped, and control. All participants engaged in numerous L2 activities during the intervention, but the gamified-flipped and flipped groups accessed them via the Moodle learning management system before each session. The gamification plug-in was activated just for the gamified-flipped group members. An explanatory mixed research method was employed using pre-and post-tests of English, a Learning Experience Questionnaire (LEQ), and a written self-report survey. The results revealed that the gamified-flipped intervention significantly enhanced language learners’ L2 achievement across all three components. Moreover, learners perceived this class as fun, traceable, collaborative, and engaging though needed an excessive workload. These findings highlight the potential of using a gamified, free learning management system to improve language acquisition. They also emphasize the importance of considering students' workloads and individual differences when implementing gamification strategies.

Target Damage Calculation Method of Nash Equilibrium Solution Based on Particle Swarm between Projectile and Target Confrontation Game

In order to scientifically evaluate the target damage effect of uncertain intersection between projectile and target confrontation game, this paper establishes a basic model of target damage in the intersection between projectile and target game, which is through the spatial relationship between the explosive position of the projectile and the target position and gives a calculation method of target damage evaluation. The study applies Nash equilibrium theory to analyze income-loss functions in an adversarial game model. According to the definition of the possibility degree of interval number in uncertain multi-attribute decision-making, we proposed a method of solving the Nash equilibrium value of the income matrix of both sides of the game, which is using the interval possibility degree. We used quantitative calculations to analyze game strategies and changes in target damage under different states, such as fragment numbers, velocities, and angles of hit in the confrontation game between projectile and target. Based on the Nash equilibrium value solution method of the particle swarm optimization algorithm, we quantitatively calculated the fitness change relationship between the projectile and the target under different strategy sets. Based on the actual projectile confrontation game test, we analyze the damage probability effects of two projectiles at different explosion positions and different rendezvous angle angles on the three cabins. The results show that in the same area, the intersection angle is smaller, the probability of target damage is greater, and the intersection distance is farther, the damage probability is smaller.

Collaborative Pricing Strategy of Distribution Network and Electric Vehicle Aggregator Based on Integral System

Scaled EVs as a new type of load have great potential to participate in the standby market. However, the uncertainty of EV users' participation in aggregator regulation affects the optimal strategy for aggregators to participate in the market. Aiming at the uncertainty of users' willingness and the conflict of interest between aggregators and the distribution network, this paper proposes an optimal scheduling strategy of master-slave game for EV aggregators to improve users' willingness with a point system. First, the second-order cone optimal current model of the distribution network is established, and then the distribution network node marginal tariff is obtained. Second, the aggregator revenue model with energy storage is established, and the price-guided demand response model is proposed, while the aggregator point system is established to guide the charging behavior of EV users, so that the cooperative operation optimization problem between the distribution grid and the aggregator can be solved, and the economic interests among EV users, EV aggregators and distribution grid operators can be effectively balanced.

Exploratory testing for platform video games: strategies and lessons learned

With the growth of the digital games market, the occurrence of bugs in games has a significant impact and generates dissatisfaction among users. Therefore, conducting tests is necessary to avoid these events and ensure the quality of the distributed product. Among the tests that are performed in games, one that is particularly effective in identifying bugs from the user's perspective is exploratory testing, but this is little covered in game testing literature, not providing new testers with guides or paths to be used for specific occasions. This paper reports an experience of applying exploratory testing strategies in 2D and 3D platform games. We selected seven well-known strategies of exploratory testing and conducted a study that involved the definition of a game testing procedure and proper adaptations for the games under test. By applying the game testing procedure, several bugs with low, medium and high severity were uncovered. The lessons learned and routines carried out in this study can be used by new testers in games of the same category, in an attempt to obtain better results.

Exploiting game equilibrium mechanisms towards social trust-based group consensus reaching

In group decision making (GDM), the real or virtual moderator usually provides the suggestion and compensation based on consensus improvement for the decision maker (DM) to promote group consensus. In order to ensure DMs own interests, there are the game among DMs and the game between DM and moderator to effect the consensus reaching. In this case, we analyze the inherent game reasons and investigate the game-driven consensus model. Firstly, based on the subjective trust of social network and the evaluation consensus of DMs, and inspired by the idea of data envelopment analysis (DEA), this paper explores the quantitative relation between subjective trust and the DMs’ consensus to determine the DM’s weights. Then, under the compensation of consensus improvement, we formulate game strategy between DMs and the moderator to ensure the DM’s consensus, which impacts the cooperation behavior between DM and the moderator. Based on the game strategy, we further establish bi-level game consensus model and investigate Nash equilibrium of group consensus model. Finally, the experiments are used to illustrate the feasibility of the proposed method, and comparative analysis demonstrates its advantages.