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Background: In health care, teamwork skills are critical for patient safety; therefore, great emphasis is placed on training these skills. Given that training is increasingly designed in a blended way, serious games may offer an efficient method of preparing face-to-face simulation training of these procedural skills. Objective: This study aimed to investigate the teamwork principles that were used during gameplay by medical students and teamwork experts. Findings can improve our understanding of the potential of serious games for training these complex skills. Methods: We investigated a web-based multiplayer game designed for training students' interprofessional teamwork skills. During gameplay, 4 players in different roles (physician, nurse, medical student, and student nurse) had to share information, prioritize tasks, and decide on next steps to take in web-based patient scenarios, using one-to-one and team chats. We performed a qualitative study (content analysis) on these chats with 144 fifth-year medical students and 24 health care teamwork experts (as a benchmark study) playing the game in groups of 4. Game chat data from 2 scenarios were analyzed. For the analysis, a deductive approach was used, starting with a conceptual framework based on Crew Resource Management principles, including shared situational awareness, decision-making, communication, team management, and debriefing. Results: Results showed that most teamwork principles were used during gameplay: shared situational awareness, decision-making (eg, re-evaluation), communication (eg, closed loop), and team management (eg, distributing the workload). Among students, these principles were often used on a basic level. Among experts, teamwork principles were used with more open forms of speak up and more justification of decisions. Some specific Crew Resource Management principles were less observed among both groups, for example, prevention of fixation errors and use of cognitive aids. Both groups showed relatively superficial debriefing reflections. Conclusions: Playing a multiplayer game for interprofessional teamwork appears to facilitate the application of teamwork principles by students in all important teamwork domains on a basic level. Expert players applied similar teamwork principles on a moderately high complexity level. Some teamwork principles were less observed among both students and expert groups, probably owing to the artifacts of the game environment (eg, chatting instead of talking). A multiplayer game for teamwork training can elicit the application of important, basic teamwork principles, both among novices and experts, and provides them with a flexible, accessible, and engaging learning environment. This may create time for exercising more complex skills during face-to-face training. [ABSTRACT FROM AUTHOR]

In this paper, we investigate a model-free identifier-critic-based optimal adaptive controller for multiplayer games with the input disturbances. Specifically, we first adopt the identifier neural network to identify the system dynamics. Simultaneously, we use the critic neural network to estimate the optimal cost function thereby obtaining the estimated optimal controller. Further taking the input disturbances into consideration, we add a feedback gain into the estimated optimal controller so as to obtain the controller. The learning of the identifier and critic network is online and simultaneous. Then, we analyze the stability of the proposed approach. Eventually, the simulation results illustrate the validity of the proposed controller. [ABSTRACT FROM AUTHOR]

Real estate gives a huge impetus to the development of the national economy and surging housing prices can easily trigger social problems and financial risks. Considering housing price fluctuations, this study constructs a stochastic evolutionary game model from the perspective of the implicit interest coalitions among local governments, real estate enterprises, and speculators. The stable condition of the model is that local government, local governments decide to regulate, real estate enterprises select not to hype housing prices, and speculators choose not to buy houses. Through numerical simulation, this study finds that the supervision of central government and the regulatory strength of local governments can affect the choices of players differently, which gives a new explanation for the retaliatory rise of housing prices when local governments conduct regulation measures frequently. In addition, gray income, the punishment of hype, and the cost of speculation can make the strategic moves of players different in severity and direction under a stochastic environment. Based on these findings, countermeasures and suggestions are proposed in this study. [ABSTRACT FROM AUTHOR]

We introduce i-misère play to multiplayer impartial games, which is an extension of Li's rank-based play and, at the same time, which contains the normal play and the misère play as special cases when the number of players is two. We characterize losing positions of such plays for both NIM and Moore's game. i-misère play is defined as a special case of a more general notion of preference-based play, and we also study properties of some other preference-based plays. [ABSTRACT FROM AUTHOR]

Abstract We often try to predict others' actions by obtaining supporting information that shows a preference index of surrounding people. In order to reproduce these situations, we propose a game named "One-sided Preference Game with Reference Information (OSPG-R)." We conducted experiments in which players who have similar preferences compete for objects in OSPG-R. In the experiment, we used three different types of objects: boxes, faces, and cars. Our results show that the most frequently selected object was not the most popular one. In order to gain deeper insights into the experiment's results, we constructed a decision-making model based on two assumptions: (1) players are rational and (2) are convinced that the other players' preference orders are equivalent to the preference index for the group. Compared to the choice behavior of the model, the experiment's results show that there was a tendency to take risks when the objects were faces, or the priority of that particular player was low. Highlights • We propose a multiplayer decision-making game with reference information. • We conducted experiments where players competed for objects: boxes, faces, and cars. • The most frequently selected object was not the most popular one. • When the objects were faces, players tended to choose to take risks. • Choice behavior between risk and safe were not symmetrical. [ABSTRACT FROM AUTHOR]

Several traffic flows contain social dilemma structures. Herein, we explored a route-selection problem using a cellular automaton simulation dovetailed with evolutionary game theory. In our model, two classes of driver-agents coexist: D agents (defective strategy), which refer to traffic information for route selection to move fast, and C agents (cooperative strategy), which are insensitive to information and less inclined to move fast. Although no evidence suggests that the social dilemma structure in low density causes vehicles to move freely and that in high density causes traffic jams, we found a structure that corresponds to an n -person (multiplayer) Chicken ( n -Chicken) game if the provided traffic information is inappropriate. If appropriate traffic information is given to the agents, the n -Chicken game can be solved. The information delivered to vehicles is crucial for easing the social dilemma due to urban traffic congestion when developing technologies to support the intelligent transportation system (ITS). [ABSTRACT FROM AUTHOR]

The purpose of this study was to examine the impacts of embedding collaboration into a game with a self-explanation design for supporting the acquisition of light and shadow concepts. The participants were 184 fourth graders who were randomly assigned to three conditions: a solitary mode of the game with self-explanation, a collaborative mode with self-explanation, or the control condition of a single-user game without integrating self-explanation. Students' conceptual understanding was measured through an immediate posttest and a retention test with a three-week delay. Further, students' engagement in answering the prompts was also investigated. The findings showed that having students collaboratively play science-based games with a self-explanation design embedded was not sufficient to help them learn the science concepts. Rather, it was the level of engagement in responding to the self-explanation prompts that mattered. [ABSTRACT FROM PUBLISHER]

Background. Simulation games have proven to be useful in environmental education. In particular, the board game KEEP COOL has already successfully been used in multiple educational settings. In 2014, the new digital version of this game, KEEP COOL ONLINE 2 was developed. A special challenge was how to support interaction between players.Aim. This article investigates the potential of digital media for supporting interaction and educational experiences in multiplayer simulation games.Method. For this purpose the analog and digital versions of KEEP COOL have been compared, tested and evaluated in school classes.Results. This evaluation shows that interactivity and multimedia can be used in simulation games to provide an engaging game experience. However, compared to a face-to-face multiplayer game, drawbacks occur regarding ease of communication and traceability of interactions.Future Prospects. As the digital version is still in its prototype form, the full potential of digital media has not yet been put into practice. However, I will give some recommendations on how to make further use of interactivity and multimedia effects in order to enhance immersive player experience, as well as the achievement of relevant educational objectives. [ABSTRACT FROM AUTHOR]

Computer team games have attracted many players in recent years. Most of them are rule-based systems because they are simple and easy to implement. However, they usually cause a game agent to be inflexible, and it may repeat a failure. Some studies investigated the learning of a single game agent, and its learning capability has been improved. However, each agent in a team is independent and it does not cooperate with others in a multiplayer game. This article explores an evolution strategy for a computer team game based on Quake III Arena. The Particle Swarm Optimization (PSO) algorithm will be applied to evolve a non-player character (NPC) team in Quake III to be more efficient and intelligent. The evolution of a single NPC, which accommodates to its team and, moreover, the team has learning and cooperating abilities, will be discussed. An efficient team is composed of various members with their own specialties, and the leader is capable of evaluating the performance of a member and assigning it a proper job. Furthermore, the leader of an intelligent team will adapt a strategy appropriate for various circumstances and obtain the team's best performance. Instead of considering the tactic of an individual bot, this article takes the strategy of a team into account. [ABSTRACT FROM AUTHOR]

The popularity of massively multiplayer online role-playing games (MMORPGs) has grown enormously, with communities of players reaching into the millions. Their fantasy narratives present multiple challenges created by the virtual environment and/or other players. The games' potential for education stems from the fact that players are immersed in a virtual world where they have the opportunity to manipulate and explore, thus motivating the construction of knowledge. The interaction and collaboration between participants allows students to exchange information, test their understanding and reflect on what they have learned. Given the promising results of using MMORPG technologies for educational purposes, this paper translates the multiplayer role playing game (MRPG) aspect, the essential concept behind MMORPGs, into the classroom context. We present the abstraction behind a Classroom Multiplayer Presential Role Playing Game (CMPRPG) and the development of a CMPRPG for teaching ecology. The game has a quest structure in which each result highlights a key teaching objective. It is implemented at a high level, with interaction between reusable game elements defined using triggers. It is observed that the implemented CMPRPG has appropriate usability levels, benefits the learning and application of the concepts of ecology and, in the interactive dimensions, it encourages participation and collaborative narrative structures among participants. [ABSTRACT FROM AUTHOR]

DouDiZhu, a multiplayer game with incomplete information, is the most popular card game in China. Although there are many DouDiZhu card games in the world, the specific characteristics of classical DouDiZhu card games are a harmonious combination of player numbers, player characters (landlords and peasants), deck numbers, and scoring systems. However, research on the complexity and attractiveness of DouDiZhu has not established. Therefore, in this paper, artificial intelligence (AI) players of different levels of DouDiZhu game were constructed for research, self-game simulation was conducted for DouDiZhu AI players, and game refinement measures were used to evaluate and identify the best Settings of the game. The results show that classical DouDiZhu provides the most complex game setup for all types of DouDiZhu AI players, while also clarifying its popularity. [ABSTRACT FROM AUTHOR]