Multiple Games Research Articles

Multiple game trading strategy of multiple virtual power plants considering carbon trading

The large-scale access of distributed energy resources has a certain impact on the power grid, so distributed energy resources cannot participate in the power market transactions alone. The concept of the virtual power plant (VPP) has thus emerged, which can aggregate distributed power sources and controllable loads in a region for coordinated regulation. The trading of VPPs should not only consider the economy but also its degree of low carbon. Therefore, this paper constructs a unified bidding strategy for multi-VPPs that considers carbon–electricity integration trading. We design a multi-game trading strategy among multi-VPPs to achieve unified trading, after each VPP determines its internal trading strategy. Finally, through simulation, we verify that the multiple game strategy between multi-VPPs that considers carbon trading here proposed can effectively improve the efficiency and trading income of VPPs and promote the consumption of new energy.

Impact of game-based learning strategies in student engagement in Health Professions education

Learning is enhanced when students are involved and engaged. However, in the past years there has been an increase in student disengagement. Class attendance is decreasing and students do not complete their assignments or turn work on time. Research has demonstrated that engagement allows the student to develop higher-level critical thinking, which is essential in Health Professions. Providing opportunities for this intellectual engagement is necessary to capture the student’s attention and numerous technologies have surged to meet this goal. One of the teaching resources utilized as an engagement tool in the classroom is gamification and game-based learning strategies. In this study, we developed game based educational resources and assessed their impact in student engagement and learning in Optometry students. A total of 66 students were enrolled in a Systemic Pathology course. The course used flipped instruction and students were asked to watch pre-recorded lectures or videos on a topic before the on-site session, where they worked in groups on a case based on that topic. The game-based learning approach was used for some of the course cases and consisted in using multiple games provided by the instructor to provide a short answer for the case questions. At the end of the semester, all the students completed a feedback questionnaire. The students reported that solving cases during class, independent of the use of games, was beneficial to their learning (79%). They perceived the use of games as enjoyable (68%) and reported an improvement in their learning (69% reporting from beneficial sometimes to very often) when compared to the traditional cases without the game component. Furthermore, we compared this cohort to the same course offered the previous year with no game-based activities to determine the impact on student performance. We found that the two groups obtained similar scores in the quizzes, final exam, and course average. In conclusion, the use of game-based learning strategies increased the engagement of the students and their perception of what they learned, however, it did not seem to improve their academic performance in the course. APS Teaching Career Enhancement Award This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Multiple Game and Computer-Aided Evaluation System Design of Computer Software Legal Protection Policy

This article has deeply studied the two kinds of computer software legal protection policies ISO17799 and ISO13355, and analyzed the conceptual model and relational model of the multiple game. On this basis, the concept of computer-aided evaluation based on computer law is proposed, and a computer-aided evaluation system is designed, which effectively realizes asset evaluation based on multiple games and threat and vulnerability evaluation based on probability statistics. And on the basis of referring to the risk assessment process and based on the actual needs of the system, a highly operable assessment process that is convenient for computer implementation is proposed.

Shaken Not Stirred: The Name of the Game in the Post-Truth Condition

ABSTRACT The post-truth condition is just as much about naming a meta-game as winning it. This condition can be tracked across Western intellectual history from the Homeric epics to popular culture. The common thread is that players are more likely to succeed in this meta-game if they have a certain consistency of character, which Thomas More called “integrity.” The presence of integrity means that the historical losers have often had an advantage in defining for subsequent generations the name of the game because the steadfastness of their characters may make them be regarded as the agents of history, for better or worse. Further, naming the game tends to be stabilized by a variety of mental and material conditions, including “modal power”—control over what people think is and is not possible. Modal power is related to both Machiavellian politics and Kantian transcendentalism, and to the phenomenon of “truthiness.” The character of the post-truth player is epitomized by Thomas More, the “man for all seasons,” who remained consistent as he moved between multiple games, and ultimately to his execution.

Examining the role of cyberloafing, narcissism, locus of control, and social appearance anxiety on the Internet gaming disorder in university students

AbstractIndividual and multiple games played on the Internet are seen as a popular leisure activity, which is becoming increasingly common among young people. With the Covid 19 pandemic, the interest in Internet games has increased because young people spend more time at home. This has led to an increase in Internet gaming disorder, which is recognized by the American Psychiatric Association and the World Health Organization. When the literature is examined, attention is drawn to the importance of revealing the factors associated with Internet gaming disorder to combat Internet gaming disorder in young people. This research was carried out to reveal the Internet gaming disorder profiles of university students in Turkey and to determine the factors associated with it. In this direction, the structural relationships between cyberloafing, narcissism, locus of control, and social appearance anxiety variables, which are thought to be associated with Internet gaming disorder, were examined. The research data were obtained from 596 university students studying in various cities in Turkey. Research findings show that cyberloafing and locus of control directly affect Internet gaming disorder. Narcissism, on the other hand, has been shown to have a mediating effect on Internet gaming disorder through cyberloafing. It has been determined that Internet gaming disorder affects social appearance anxiety. Research results show that Internet gaming disorder can be reduced by improving internal locus of control and reducing cyberloafing behaviors and narcissism. The results of the research will provide tips to experts on interventions that can be made for students with Internet gaming disorder. In line with the findings obtained from the research, what can be done to combat Internet gaming disorder is discussed.

Pricing lane changes

Risky and aggressive lane changes on highways reduce capacity and increase the risk of collision. We propose a lane-changing pricing scheme as an effective tool to penalize those maneuvers to reduce congestion as a societal goal while aiming for safe driving conditions. In this paper, we first model driver behavior and their payoffs under a game theory framework and find optimal lane-changing strategies for individuals and their peers in multiple pairwise games. Payoffs are estimated for two primary evaluation criteria: efficiency and safety, which are quantified by incorporating driver tradeoffs. After that, the discretionary lane-changing (DLC) model is calibrated and validated by real-world vehicular trajectory data. To manipulate drivers’ DLC behaviors, two types of lane-changing tolls based on local-optimal and global-optimal rules are introduced to align individual preferences with social benefits. We find prices can close this gap and achieve ‘win-win’ results by reducing drivers’ aggressive lane changes in the congested traffic. Meanwhile, the tolls collected can be used to compensate drivers who get delayed when yielding, to encourage appropriate yielding behavior and a pseudo-revenue neutral tolling system.

Increasing Detections of Small to Medium-Sized Mammals Using Multiple Game Cameras

Surveys using game cameras have become a standard method to determine presence of small to medium-sized mammals such as Spilogale putorius (Eastern Spotted Skunk). Surveys typically use a single camera at each site; however, prior work with other species suggests that single cameras are less likely than multiple cameras to detect small and mid-size mammals. During the winters of 2019–2020 and 2020–2021, we placed 71 camera stations in the Arkansas Ozarks, with each station having 2 cameras aimed at the same target. We used detections of a common species, Sciurus carolinensis (Eastern Gray Squirrel), to determine if adding an additional camera at each site could increase detections of animals similar in size. Overall, detections increased 27% with the addition of a second camera. We found that a second camera increased Eastern Gray Squirrel detections by 15.6% when the animal was closer than 1 body-length from the target bait tree and 41.2% when the animal was more than 1 body-length from the bait tree. We conclude that a single camera may miss a significant number of detections of small to medium-sized animals and could bias results of detection/non-detection studies.

Joint Channel Access and Power Control Optimization in Large-Scale UAV Networks: A Hierarchical Mean Field Game Approach

With the development of the new generation communications, unmanned aerial vehicle base stations (UAV-BSs) have been widely used to enhance the terrestrial communications. Because of the increasing demand for communication, the scale of the networks is constantly expanding. This paper investigates a joint channel access and power control optimization in large-scale UAV coverage networks, and proposes a hierarchical mean field game approach. Firstly, considering the quality of service (QoS) requirement and energy consumption, the cost function of UAV-BSs is designed. A joint channel access and power control optimization framework is built to improve the efficiency of resource utilization by minimizing the cost function. Then, the joint optimization problem is formulated as a hierarchical game model whose exterior is a channel access game and interior is a multiple mean fields game (MMFG). The channel access game is proved to be a potential game and have at least one Nash Equilibrium (NE). As feedback for channel access optimization, power control policy is obtained by MMFG. A near-optimal algorithm for joint optimizations is designed to achieve the NE. Besides, aiming to the networks with limited computing capability, we also propose a fast optimization approach. The original optimization problem is divided into two independent sub-problems. The channel optimization sub-problem is formulated as a local altruistic game model, then the power policy sub-problem is solved by MMFG. A distributed algorithm based on spatial adaptive play and better response is proposed. Finally, the simulation results verify the validity of the proposed algorithms. The near-optimal optimization method has a better performance while the fast optimization method has a higher speed of the convergence. Compared with the traditional approaches, both of them improve resources utilization efficiency greatly.

Pelatihan Membuat Soal Matematika Secara Daring Menggunakan Aplikasi Kahoot

In the world of education, it is currently commonly known as learning media that can be used as teaching aids for teachers to make it easier for students to understand the material being taught. Learning media can clarify the presentation of messages and information from teachers to students so that they can facilitate and improve learning processes and outcomes. Learning media can also overcome the limitations of the senses, space and time. One form of media is Kahoot. Kahoot is a game-based learning platform that uses educational technology in schools and other educational institutions. Kahoot is in the form of a multiple choice quiz game application or a survey of opinions and opinions of students or members of public institutions conducted online via a browser. learning through Kahoot is very easy to implement. Kahoot can be played through different browsers to browsers installed on student gadgets. The Kahoot application is very interesting, because the teacher can create quizzes by inserting pictures or videos in the problem so that it can attract students' attention. Kahoot is because the question is equipped with an image or video. Even using short expressions, accompanied by music that is appropriate for their age. Not only that, kahoot can be a form of online interactive media.

Autonomous collaborative combat strategy of unmanned system group in continuous dynamic environment based on PD-MADDPG

In this study, we studied the Unmanned System Group (USG) Autonomous Collaborative Combat Strategy (ACCS) and the Parallel Decoupling-Multi-agent Deep Deterministic Policy Gradient (PD-MADDPG) algorithm was proposed. This was conducted on the background of USG confrontation game problem between ourselves and the enemy in the continuous dynamic environment of future aerial combat. An independent Parallel Benchmark Critic (PB-Critic) network and Parallel Decoupling Critic (PD-Critic) network for each member of USG was developed to maximize the whole reward and member reward of USG under time parallelism. By integrating Forerunner Mechanism (FM) into the parallel decoupling reward function, the reward sparse problem confronted by parallel decoupling reward function at the beginning of the algorithmic training was controlled as well as the convergence efficiency of USG in the continuous dynamic environment of future aerial combat. Introduction of Symmetric Attention Mechanism (SAM) to the Critic-network and Actor-network shortened the screening radius of helpful information related to the confrontation game of USG of both sides of ourselves and enemy in the continuous dynamic environment. Assuming the multiple typical confrontation game control strategies of USG of the enemy in the continuous dynamic environment, the effectiveness and feasibility of the strategies were simulated and verified. The PD-MADDPG improved the multiple inadequate natural endowments of Multi-Agent Deep Deterministic Policy Gradient (MADDPG) which showed that the policy training convergence and execution stability were greatly enhanced, and USG’s behavioral autonomy for collaborative combat in the continuous dynamic environment was further improved.

Potential-Game-Based 5G RAN Slice Planning for GBR Services

The Radio Access Network (RAN) slice planning is a key phase within the RAN slice management and orchestration process. Based on the performance requirements of requested RAN slices and key performance indicators of the RAN and existing RAN slices, the RAN slice planning mainly consists of deciding (a) the feasibility of deploying new RAN slices; (b) re-configuring the existing RAN accordingly; and (c) the need to renegotiate the Service Level Agreements (SLAs) and/or expand the RAN (i.e., radio resources, carriers, cells etc) if one or more RAN slices cannot be accommodated in a first attempt. Under this context, we propose a framework for planning RAN slices which require their data sessions get a Guaranteed Bit Rate (GBR) and the probability of blocking such sessions is below a threshold. To meet such requirements, our framework plans the amount of prioritized radio resources for new and already deployed RAN slices. We formulate the RAN slice planning as multiple ordinal potential games and demonstrate the existence of a Nash Equilibrium solution which minimizes the average probability of blocking data sessions for all the RAN slices. We perform detailed simulations to demonstrate the effectiveness of the proposed solution in terms of performance, and renegotiation capability.

ENABLING FLEXIBLE AND ADAPTABLE NAVIGATION OF GROUND ROBOTS IN DYNAMIC ENVIRONMENTS WITH LIVE LEARNING

Federated learning is utilized for automated ground robot navigation, enabling decentralized training and continuous model adaptation. Strategies include hardware selection, ML model design, and hyperparameter fine-tuning. Real-world application involves optimizing communication protocols and evaluating performance with diverse network conditions. Federated learning shows promise for machine learning-based life learning systems in ground robot navigation. Research objective: to explore the use of federated learning in automated ground robot navigation and optimize the system for improved performance in dynamic environments. Materials and methods. The research utilizes federated learning to train machine learning models for ground robot navigation. Hardware selection, ML model design, and hyperparameter fine-tuning are employed. Communication protocols are optimized, and performance is evaluated using multiple gaming machine algorithms. Results. The results show that decreasing the learning rate and increasing hidden units improve model accuracy, while batch size has no significant impact. Communication protocols are evaluated, with Protocol A providing high efficiency but low security, Protocol B offering a balance, and Protocol C prioritizing security. Conclusion. The proposed approach using federated learning enables ground robots to navigate dynamic environments effectively. Optimizing the system involves selecting efficient communication protocols and fine-tuning hyperparameters. Future work includes integrating additional sensors, advanced ML models, and optimizing communication protocols for improved performance and integration with the control system. Overall, this approach enhances ground robot mobility in dynamic environments.

Game Learning Analytics:

Game learning analytics (GLA) comprise the collection, analysis, and visualization of player interactions with serious games. The information gathered from these analytics can help us improve serious games and better understand player actions and strategies, as well as improve player assessment. However, the application of analytics is a complex and costly process that is not yet generalized in serious games. Using a standard data format to collect player interactions is essential: the standardization allows us to simplify and systematize every step in developing tools and processes compatible with multiple games. In this paper, we explore a combination of 1) an exploratory visualization tool that analyzes player interactions in the game and provides an overview of their actions, and 2) an assessment approach, based on the collection of interaction data for player assessment. We describe some of the different opportunities offered by analytics in game-based learning, the relevance of systematizing the process by using standards and game-independent analyses and visualizations, and the different techniques (visualizations, data mining models) that can be applied to yield meaningful information to better understand learners’ actions and results in serious games.

Usability Evaluation of Imikode Virtual Reality Game to Facilitate Learning of Object-Oriented Programming

Many empirical studies have shown that educational games and recent technologies impact education and increase learning effectiveness, students’ motivation and engagement. The overall aim of this study is to evaluate the usability of Imikode, a virtual reality (VR) game that was developed to introduce the concepts of object-oriented programming to novices. The improved version of the Imikode VR game consists of three features: An artificial intelligence component designed to provide real-time error feedback to users, an intelligent agent that guides and teaches users how to play the game and finally, the integration of multiple game play that gives learners more opportunities to explore the VR environment for greater immersive learning experience. This study adopted a survey approach and recruited first-year computer science students to measure learner satisfaction with educational virtual reality games and examined the correlations among the attributes of the Usefulness, Satisfaction and Ease of Use questionnaire of usage of Imikode. The results showed that the students were satisfied with Imikode and perceived the virtual reality educational game as very useful for learning object-oriented programming concepts. In addition, there was a correlation among the questionnaire variables, which means that researchers can use the instrument for future usability studies in the context. We further proffered some design recommendations for building software tools.

Unlicensed band sharing based on bargaining game for heterogeneous networks

Currently, limited licensed frequency bands cannot meet the increasing demands for various wireless communication applications any more. It is necessary to extend wireless communication networks to unlicensed spectrum. In this paper, we propose a new bargaining framework for unlicensed band access to achieve high spectrum efficiency, where one radio access technology (RAT) (such as macro cellular network) "competes" the unlicensed bands with multiple other RATs (such as small cellular networks or Wi-Fi) virtually. Considering that macro cell can share unlicensed frequencies with multiple small cells which are in the same coverage area for more freedom, we use bargaining game theory to fairly and effectively share the unlicensed spectrum between macro and multiple heterogeneous small cell networks, where bargaining loss and time dissipation loss for virtual "price" of unlicensed bands are mainly considered. In the one-to-many bargaining process, we also develop a multiple RAT alliance game strategy to reduce transmission loss in a joint manner. Simulation results show that the proposed unlicensed band sharing algorithm significantly improves the spectrum efficiency performance compared with the other practical schemes for heterogeneous networks.

Deployment Optimization of Defense Stations in an Attack-Defense Aerial War Game

An attack–defense aerial war game is developed to maximize the capability of defense forces in protecting a target area from an attack force. The attack force is composed of multiple attack stations in slant or pincer formation, and each attack station can launch multiple attackers against the defense force. The defense force is composed of one major post, two minor posts, and multiple defense stations, and each defense station is equipped with multiple interceptors against the approaching attackers. Four optional defense goals are proposed to guide the deployment of defense stations. A particle swarm optimization (PSO) algorithm is applied to acquire an optimal deployment plan of all the defense stations, under a specific defense goal. Multiple games are played with the optimal deployment plan, and the results are analyzed statistically. Interesting outlier cases are also inspected to gain more insights on the nature of the games.

Values throughout the Game Space

Pro-social themes like empathy and wellbeing are gaining popularity within games to contrast the medium's stigmatised explorations of themes like violence, often to catalyse reflection or even change players' beliefs surrounding complex scenarios. These abstract themes can be classified as a game's "values": elements that are "useful or important" to game designers and their audience. Exploration of values in games was spearheaded by Values at Play, which described how values manifest in games and advocated for their explicit consideration within games and their discourse. Frameworks such as empathy, ethical, and anti-oppressive design all discuss similar values, but present ontological diversity that makes it difficult to collate games and frameworks under the values-conscious design umbrella. Such frameworks also often focus on design approaches and small-scale prototypes but pay less attention to how values influence aspects like publication and play. As such, the present research examines values within each stage of a game's lifecycle: design, development, publication, interaction, and reportage. Doing so provides an understanding of values in multiple game contexts. This provides new perspectives that form the basis of a taxonomy for values in games, which is then presented alongside practical questions for values-conscious designers, publishers, and players to guide its use.

NCAlt: Alternatives and Difference Visualizations for Behavior Trees in Game Development Learning

When learning how to develop AI behavior it is common for students to test different ideas before settling on a desired outcome. This functionality is not available in modern behavior tree systems beyond the traditional methods of duplication and conditional execution. We present NCAlt - a visual behavior authoring framework for exploring multiple game AI behaviors. The system also allows selective merging of nodes between multiple behavior tree alternatives and their visual differencing in the game scene and behavior tree views. We present two worked examples which demonstrate how NCAlt can improve the workflow of game development students. We have conducted a user study where NCAlt was compared against NodeCanvas for creating and using alternatives with moderately skilled game development students. Participants rated NCAlt 15% higher than NodeCanvas on the System Usability Scale (SUS). NCAlt was also rated higher on a self-developed questionnaire. We also conducted a semi-structured interview and a detailed longitudinal follow-up and obtained mostly positive feedback for NCAlt. The results suggest NCAlt has a potential to improve the types of workflows with behavior trees that involve exploration for game development students and facilitate learning, as a result. NCAlt was built on top of NodeCanvas - a plugin for the Unity Engine.

Endogenous timing, strategic tariff game and bilateral trade in vertical oligopoly

AbstractThis paper builds an observable delay game in endogenous timing to study the possible occurrence of trade wars in a vertical, bilateral trade model. It examines the effects of production cost differences and order of moves on optimal tariffs, market equilibria, dumping margin and social welfare in both fixed timing and endogenous timing games. In a fixed timing game, it shows that price dumping in the intermediate good market arises from differences in country‐specific final good production costs. Different from Bernhofen (1995; Journal of International Economics), trade costs resulting from reciprocal tariffs in upstream markets can reverse the price dumping under certain conditions. In an endogenous timing game, this paper finds that the magnitude of cost differences significantly influences countries’ decisions on the order of moves in a strategic tariff‐making game. Both countries want to be the first movers under a small cost difference and consequently reach a simultaneous equilibrium result. This demonstrates that the second‐best equilibrium proceeds under certain conditions. Under a large cost difference, sequential policy‐making is the subgame perfect Nash equilibrium. If the cost difference is sufficiently large, both countries have an incentive to launch a trade war as a multiple equilibrium game.

Spatial ecology, optimal control and game theoretical fishing problems.

Of paramount importance in both ecological systems and economic policies are the problems of harvesting of natural resources. A paradigmatic situation where this question is raised is that of fishing strategies. Indeed, overfishing is a well-known problem in the management of live-stocks, as being too greedy may lead to an overall dramatic depletion of the population we are harvesting. A closely related topic is that of Nash equilibria in the context of fishing policies. Namely, two players being in competition for the same pool of resources, is it possible for them to find an equilibrium situation? The goal of this paper is to provide a detailed analysis of these two queries (i.e optimal fishing strategies for single-player models and study of Nash equilibria for multiple players games) by using a basic yet instructive mathematical model, the logistic-diffusive equation. In this framework, the underlying model simply reads [Formula: see text] where K accounts for natural resources, [Formula: see text] for the density of the population that is being harvested and [Formula: see text] encodes either the single player fishing strategy or, when dealing with Nash equilibria, a combination of the fishing strategies of both players. This article consists of two main parts. The first one gives a very fine characterisation of the optimisers for the single-player game where one aims at solving [Formula: see text], under [Formula: see text] and [Formula: see text] constraints on the fishing strategies [Formula: see text]. In particular, we show that, depending on the value of these constraints, this optimal control problem may behave like a convex or, conversely, concave problem. We also provide a detailed analysis of the large diffusivity limit of this problem. In the case where two players are involved, we rather write [Formula: see text] as [Formula: see text] where [Formula: see text], the fishing strategy of the i-th player, also satisfies [Formula: see text] and [Formula: see text] constraints. Defining [Formula: see text] we aim at finding a Nash equilibrium. We prove the existence of Nash equilibria in several different regimes and investigate several related qualitative queries, for instance providing examples of the well-known tragedy of commons. Our study is completed by a variety of numerical simulations that illustrate our results and allow us to formulate open questions and conjectures.