Set Of Games Research Articles

Risk-dominant Equilibrium in Chicken and Stag-hunt Games with Different Dilemma Strengths

Objective: This study investigates how the strength of risk-averting and gamble-intending dilemmas can affect the risk-dominant equilibrium in chicken and stag-hunt games. The focus is on deriving conditions under which the risk-dominant equilibrium can maximize group benefit. Methods: Basic concepts of game theory were used to determine the Nash equilibria of each game.The Harsanyi-Selten theory was applied to determine the risk-dominant equilibrium. By manipulating the parameters related to risk-averting and gamble-intending dilemmas, different game scenarios are sim-ulated to observe the resulting equilibria. Results: The study found that for a given strength of the risk-averting dilemma, the risk-dominant equilibrium in the chicken game continuously shifts towards mutual cooperation as the strength of the gamble-intending dilemma decreases. In the SH game, the degree of cooperation of the risk-dominant equilibrium for a given strength of the gamble-intending dilemma decreases with increasing strength of the risk-averting dilemma and takes the discrete values: 1, 0.5 and 0. The results show the importance of understanding individual risk preferences to predict strategic decisions and equilibrium outcomes in these game settings. Conclusion: The risk-dominant equilibrium in chicken and stag-hunt games depends on the relative strength of the risk-averting and gamble-intending dilemmas. By taking these factors into account, researchers and policy makers can better predict the likely outcomes of strategic interactions in scenarios with conflicting interests and different risk tolerances. The risk-dominant equilibrium can provide reasonable solutions to real-world conflict situations, such as the Iran-Iraq conflict over shared oil and gas resources.

Assumed similarity in personality traits has a short-lived positive effect on cooperation in absence of reciprocity

ABSTRACT Social dilemmas employing finitely repeated public goods games are usually employed to study individual cooperation and free-riding. It remains unclear whether and to what extent cooperation in teams is motivated by the assumed similarity of personality traits of other players. This study aims to investigate the effectiveness of sharing information on personality traits in a voluntary contribution mechanism game environment, which includes a punishment mechanism, on individual contributions to a public good. These factors are integrated into a game setting that incorporates various group sizes embedded in a survey. In addition, the participants in this study were selected from the general population of the United Arab Emirates. Results show that, on average, respondents were positively affected by the similar personality traits treatment in the first round soon after the treatment.

Social identification and paranoia.

Paranoia is associated with variation in social behaviour, such as lower inclination to trust others or to behave generously in economic game settings. Such variation may stem, in part, from a reduced tendency to socially identify with others, although previous studies have reported mixed results. We tested whether paranoia involves altered social identification in a pre-registered online study investigating the relationship between a measure of social identification, paranoia, and social behaviours in economic games. We successfully manipulated social identification, but paranoia was associated with slightly increased social identification overall. Neither paranoia nor social identification predicted behaviour in the economic games, and there was no interaction between paranoia and social identification regarding trusting and cooperative behaviours. Our results converge with recent work suggesting that more paranoid individuals may harbour a higher tendency to perceive themselves as having similar beliefs to others. We discuss some key areas for future research to progress understanding in this area.

Intelligent Agents and Causal Inference: Enhancing Decision-Making through Causal Reasoning

This study examines the incorporation of causal inference methods into intelligent entities and examines the benefits of utilizing causal reasoning to improve decision-making procedures. This study entails conducting an experimental evaluation within a video game setting to evaluate the performance of three separate agent types: ExplorerBOT, GuardBOT, and CausalBOT. The ExplorerBOT utilizes a stochastic path selection technique for task completion, whereas the GuardBOT remains immobile yet exhibits exceptional proficiency in identifying and neutralizing other bots. On the other hand, the CausalBOT utilizes sophisticated causal inference methods to examine the underlying factors contributing to the failures noticed in the task completion of the ExplorerBOT. The aforementioned feature allows CausalBOT to make informed decisions by selecting paths that have a greater likelihood of achieving success. The main purpose of these experiments is to assess and compare the effectiveness of two distinct bots, namely ExplorerBOT and CausalBOT, in accomplishing their respective objectives. To facilitate comparison, two iterations of the ExplorerBOT are utilized. The initial iteration is predicated exclusively on stochastic path selection and necessitates a more profound understanding of the variables that impact the achievement of tasks. On the other hand, the second version integrates an algorithm for informed search. In contrast, CausalBOT employs causal inference techniques to discover the underlying causes of failures exhibited by ExplorerBOTs and collect pertinent data. Through the process of discerning the fundamental causal mechanisms, CausalBOT is able to make well-informed decisions by selecting pathways that maximize the probability of successfully completing a given job. The utilization of this approach greatly boosts the decision-making powers of CausalBOT, hence enabling it to effectively adapt and overcome problems in a more efficient manner when compared to alternative agents.

Analysis of the Difference Between PUBG and FORTNITE

PUBG is a video game that was created in South Korea. Following its release, the book gained significant popularity in East Asia and was subsequently acquired by China's Tencent Game Company. Subsequently, it underwent revisions and transformed into the Chinese iteration of PUBG, renowned as "Peace Elite". FORTNITE originated in North Carolina, USA, and has gained immense popularity across the United States. The purpose of this study is to conduct a comparative analysis of 100-player competitive games in East Asia and the United States. The study will utilize game interaction comparison, game design comparison, and game experience comparison to identify the strengths and weaknesses of each region. The study's findings indicate that PUBG's earlier publication has resulted in a more thorough and realistic system and game settings. The cartoonish aesthetics and futuristic elements of FORTNITE have garnered significant appeal among a large number of young players.

Characterization of Running Intensity in Canadian Football Based on Tactical Position.

This study aimed to use a data-driven approach to identify individualized speed thresholds to characterize running demands and athlete workload during games and practices in skill and linemen football players. Data were recorded from wearable sensors over 28 sessions from 30 male Canadian varsity football athletes, resulting in a total of 287 performances analyzed, including 137 games and 150 practices, using a global positioning system. Speed zones were identified for each performance by fitting a 5-dimensional Gaussian mixture model (GMM) corresponding to 5 running intensity zones from minimal (zone 1) to maximal (zone 5). Skill players had significantly higher (p < 0.001) speed thresholds, percentage of time spent, and distance covered in maximal intensity zones compared to linemen. The distance covered in game settings was significantly higher (p < 0.001) compared to practices. This study highlighted the use of individualized speed thresholds to determine running intensity and athlete workloads for American and Canadian football athletes, as well as compare running performances between practice and game scenarios. This approach can be used to monitor physical workload in athletes with respect to their tactical positions during practices and games, and to ensure that athletes are adequately trained to meet in-game physical demands.

PERANCANGAN CARDGAME LAGU DAERAH SUMATRA UTARA SEBAGAI MEDIA EDUKASI BAGI ANAK BERUSIA 12-15 TAHUN

Indonesia is consisting of 34 provinces with their own customs and folk songs. North Sumatra is one of the provinces with diverse and meaningful folk songs. Today, the influence of modern music has begun to shift the position of folk music, whereas folk songs are a national culture that can significantly foster a sense of patriotism in the younger generation of Indonesia and have moral values that need to be passed on to the younger generation. As an education media, card games can be the problem solver considering that card games are more fun, and able to stimulate children to unconsciously learn. The design is carried out through several stages, including the data collection through distributing questionnaires to the target market, conducting existing and comparative studies of other education media, literature studies through books and journals, interviews with experts, experimental research, and playtesting on the target segment, namely students aged 12-15 years old. The result of this design is a set of card game to introduce North Sumatra folk song with mechanics that are: set collecting, hand management, card drafting and singing. Two to four players engaged in the gameplay lasting 20-40 minutes long. The game has two card categories with the mission of collecting and sorting song cards according to the objective. Through one to three games, players gain experience and are able to remember 1 to 3 titles of North Sumatra folk songs. Keywords: folk songs, North Sumatra, experimental research, playtesting, cardgame, education media

The application of reinforcement learning algorithms in intelligent games

The ongoing advancement of computer hardware has spurred the need for high-quality gaming experiences. The pressing need for intelligent games, the proven efficacy of reinforcement learning in various fields, the advancement of deep reinforcement learning technology, and the desire for customized gaming experiences collectively propel the ongoing exploration and implementation of reinforcement learning in intelligent games.This study use algorithms sourced from the literature review to examine multiple facets of reinforcement learning, including the modeling of game settings, reward design, the utilization of deep learning approaches, and adversarial learning. The utilization of reinforcement learning in intelligent games holds immense importance. Initially, it enhances the gaming encounter and renders intelligent games more demanding and tailored. Furthermore, the game gains complexity and intrigue by the implementation of intelligent adversaries capable of assimilating and adjusting to the player's tactics. This not only fosters the advancement of AI technology through research and development, but also broadens the utilization of reinforcement learning in other domains, like autonomous driving and smart manufacturing. Furthermore, tailored encounters, cutting-edge game design, and utilization in schooling are likewise significant contributions, rendering reinforcement learning a pivotal element in propelling technological advancement and augmenting user experience.

Data Poisoning to Fake a Nash Equilibria for Markov Games

We characterize offline data poisoning attacks on Multi-Agent Reinforcement Learning (MARL), where an attacker may change a data set in an attempt to install a (potentially fictitious) unique Markov-perfect Nash equilibrium for a two-player zero-sum Markov game. We propose the unique Nash set, namely the set of games, specified by their Q functions, with a specific joint policy being the unique Nash equilibrium. The unique Nash set is central to poisoning attacks because the attack is successful if and only if data poisoning pushes all plausible games inside it. The unique Nash set generalizes the reward polytope commonly used in inverse reinforcement learning to MARL. For zero-sum Markov games, both the inverse Nash set and the set of plausible games induced by data are polytopes in the Q function space. We exhibit a linear program to efficiently compute the optimal poisoning attack. Our work sheds light on the structure of data poisoning attacks on offline MARL, a necessary step before one can design more robust MARL algorithms.

Human-Guided Moral Decision Making in Text-Based Games

Training reinforcement learning (RL) agents to achieve desired goals while also acting morally is a challenging problem. Transformer-based language models (LMs) have shown some promise in moral awareness, but their use in different contexts is problematic because of the complexity and implicitness of human morality. In this paper, we build on text-based games, which are challenging environments for current RL agents, and propose the HuMAL (Human-guided Morality Awareness Learning) algorithm, which adaptively learns personal values through human-agent collaboration with minimal manual feedback. We evaluate HuMAL on the Jiminy Cricket benchmark, a set of text-based games with various scenes and dense morality annotations, using both simulated and actual human feedback. The experimental results demonstrate that with a small amount of human feedback, HuMAL can improve task performance and reduce immoral behavior in a variety of games and is adaptable to different personal values.

Trust development in online competitive game environments: a network analysis approach

Trust seems to become established even in scenarios where the prerequisites for trust are complicated by conditions that evoke scepticism. Nonetheless, trust emerges, a phenomenon that is to be comprehended and examined in the present experimental inquiry. In order to comprehensively capture the process, a competitive online game environment was used to document the development of trust networks, directionality, and strength using network analysis. Despite the conditions conducive to distrust in this game setting, acts of trust were exhibited.Robust trust bonds persisting over the course of gameplay appear to manifest mostly dyadic or triadic, with participant embeddedness within the network and homophily in terms of general trustfulness towards strangers being conducive factors for trust bonding and game survivability. This study hence contributes to the overall understanding of online trust development and offers several further research opportunities in a mostly unexplored field.

How reaching numerical roundness on subgoals affects the completion of superordinate goals

This research shows that consumers who achieve numerically round subgoals perceive a higher chance of accomplishing their superordinate goals compared to those who achieve numerically non-round subgoals. This effect is mediated by the shifts in motivation to accomplish superordinate goals. First, a pilot test presents preliminary evidence from a large secondary dataset of runners’ timings in a 10 k race. Then, the effect is tested in a series of scenario-based experiments by manipulating the round vs. non-round subgoals in a variety of contexts, such as credit score, running, weight loss and loyalty programs. This is followed by a study employing a game setting where participants make guesses and earn (round vs. non-round) points. Next, a field experiment manipulates the grading method of student exams in an actual course to utilize round vs. non-round scales. Finally, a lab experiment demonstrates the moderating role of goal progress level.

The Illusion of Accuracy: Simulating the Cruades in &lt;i&gt;Ancestors legacy&lt;/i&gt; (2018)

Ancestors Legacy is a 2018 real-time strategy video game set in the Middle Ages. The game was developed by the controversial Polish studio Destructive Creations, whose members have been accused of sympathizing and supporting far-right movements online. This ideological support can be seen in their video games, some of which have included the use of crusading medievalisms in line with the civilizational-conflict narrative often espoused by far-right movements: i.e. the idea that Christianity is embarked in a never-ending battle with Islam and other cultures, of which the crusades are just an episode. Nevertheless, Ancestors Legacy categorically condemns the crusades as unfair and hypocritical, a statement backed by a reasonably accurate recounting of the events. Upon closer inspection, however, this accuracy is superficial and molded by a larger ideological framework about the Middle Ages and the crusades. This article argues that the game occupies the dissonant space of condemning crusading but adopting it as an unavoidable part of the clash of civilizations that lasts until this day, hence justifying the player exercising the very violence it condemns. This is an example of what Adrienne Shaw has called the tyranny of realism: an obsession with accuracy in video games, based on hegemonic and audience-driven perceptions of the past that do not allow for a critical and emancipatory approximation to history.Banner image taken from the press kit for Ancestors Legacy (2018, Destructive Creations).

Learning Newtonian mechanics with an intrinsically integrated educational game

AbstractBackgroundResearch on cognitive effects of educational games in general shows promising results. However, there are large variations in learning outcomes between individual educational games. Research on the design process and different design elements of educational games has led to some interesting directions, but some design aspects remain unclear.ObjectivesWe examined how an educational game designed on the basis of intrinsic integration theory, based on a strong alignment between game and learning goals, supports the learning of Newtonian mechanics.MethodsThis study applied a mixed‐methods approach (N = 223). A pre‐ and post‐test design was used to examine possible learning and transfer effects fostered by playing the educational game, Newton's Race. To examine how players played the game, log data for each player were digitally recorded during gameplay.Results and ConclusionsOur findings demonstrated a significant positive learning effect of Newton's Race (p = 0.003, d = 0.201). This can be explained through the acquired log data. Log data show that players' gameplay mostly matched expected learning during the game, with physically correct game settings occurring more and more as gameplay progressed. The ability to transfer learned knowledge to other situations was shown to be limited to situations closely resembling the game environment.ImplicationsSimilarly, designed intrinsically integrated games on different (physics) subjects could also foster learning in a relative short time. To foster transfer to other situations we propose embedding the game within other instructional activities.

A Construction of Physical Activity Games to Develop Physical Health of Kindergarten Aged 5-6 Years in Tianhe District, Guangzhou City

Background and Aim: Research on children's sports activity and games is an emerging research field in China. Relevant research results show that the development of children's basic physical qualities has not effectively penetrated the field of children's learning. The goal of this study is to establish a set of children's sports activity games, to better promote the healthy growth of children. Materials and Methods: This study adopted the interview method, questionnaire survey method, and children fitness test, through the questionnaire survey of 20 preschool teachers, and 20 kindergarten principals, and the physical health test, established the basic children's sports activity games with 7 experts; interviewed 19 focus group experts, determine the basic sports activity games; after verification by 9 appreciation experts, finally determined the sports activity games. The research process involves organizing the current problems in children's physical education, first establishing children's physical activity games, and then verifying them using focus groups and appreciation groups. The obtained data were processed using EXCELL and SPSS26.0. Results: Established physical activity games for children aged 5-6 years, contains 24 games with 6 qualities. The development of speed quality of the game: paste, eagle catch a chicken, fishing, electricity; The development of sensitive quality of the game is: feet continuous jump, fun to play sheep corner kick, jump bamboo pole, two people three feet; The development of balance quality of the game are: balance beam, everyone together to repair the bridge, single foot standing, play tires; The development of coordinated quality of the game is: standing long jump, bag jump, hopscotch, skipping music; The development of strength quality of the game are: tennis throw far, small bomb, newspaper play more and more, sandbag a thing to play; The development of flexible quality of the game are: sitting body forward bending, flipping pancakes, flipping saw pull big saw, guessing first move. Conclusion: Through the establishment of a children's sports game system, children can develop and improve their sports quality in an all-round way in an interesting and challenging environment. This is of great significance for the practice of preschool physical education and provides a useful reference for future research and practice.

Key Committing Attacks against AES-based AEAD Schemes

Recently, there has been a surge of interest in the security of authenticated encryption with associated data (AEAD) within the context of key commitment frameworks. Security within this framework ensures that a ciphertext chosen by an adversary does not decrypt to two different sets of key, nonce, and associated data. Despite this increasing interest, the security of several widely deployed AEAD schemes has not been thoroughly examined within this framework. In this work, we assess the key committing security of several AEAD schemes. First, the AEGIS family, which emerged as a winner in the Competition for Authenticated Encryption: Security, Applicability, and Robustness (CAESAR), and has been proposed to standardization at the IETF. A now outdated version of the draft standard suggested that AEGIS could qualify as a fully committing AEAD scheme; we prove that it is not the case by proposing a novel attack applicable to all variants, which has been experimentally verified. We also exhibit a key committing attack on Rocca-S. Our attacks are executed within the FROB game setting, which is known to be one of the most stringent key committing frameworks. This implies that they remain valid in other, more relaxed frameworks, such as CMT-1, CMT-4, and so forth. Finally, we show that applying the same attack techniques to Rocca and Tiaoxin-346 does not compromise their key-committing security. This observation provides valuable insights into the design of such secure round update functions for AES-based AEAD schemes.

Lifelong Dual Generative Adversarial Nets Learning in Tandem.

Continually capturing novel concepts without forgetting is one of the most critical functions sought for in artificial intelligence systems. However, even the most advanced deep learning networks are prone to quickly forgetting previously learned knowledge after training with new data. The proposed lifelong dual generative adversarial networks (LD-GANs) consist of two generative adversarial networks (GANs), namely, a Teacher and an Assistant teaching each other in tandem while successively learning a series of tasks. A single discriminator is used to decide the realism of generated images by the dual GANs. A new training algorithm, called the lifelong self knowledge distillation (LSKD) is proposed for training the LD-GAN while learning each new task during lifelong learning (LLL). LSKD enables the transfer of knowledge from one more knowledgeable player to the other jointly with learning the information from a newly given dataset, within an adversarial playing game setting. In contrast to other LLL models, LD-GANs are memory efficient and does not require freezing any parameters after learning each given task. Furthermore, we extend the LD-GANs to being the Teacher module in a Teacher-Student network for assimilating data representations across several domains during LLL. Experimental results indicate a better performance for the proposed framework in unsupervised lifelong representation learning when compared to other methods.

Controlling human causal inference through in silico task design

Learning causal relationships is crucial for survival. The human brain's functional flexibility allows for effective causal inference, underlying various learning processes. While past studies focused on environmental factors influencing causal inference, a fundamental question remains: can these factors be manipulated for strategic causal inference control? This paper presents a task control framework for orchestrating causal learning task design. It utilizes a two-player game setting where a neural network learns to manipulate task variables by interacting with a human causal inference model. Training the task controller to generate experimental designs, we confirm its ability to accommodate complexities of environmental causal structure. Experiments involving 126 human subjects successfully validate the impact of task control on performance and learning efficiency. Additionally, we find that task control policy reflects the intrinsic nature of human causal inference: one-shot learning. This framework holds promising potential for applications paving the way for targeted behavioral outcomes in humans.

Pure Nash equilibrium and independent dominating sets in evolutionary games on networks

Pure Nash equilibrium and independent dominating sets in evolutionary games on networks

On principle of optimality for safety-constrained Markov Decision Process and p-Safe Reinforcement Learning ⋆

We study optimality for the safety-constrained Markov decision process which is the underlying framework for safe reinforcement learning. Specifically, we consider an undiscounted safety-constrained Markov decision process subject to random stopping times. The decision maker's goal is to reach a goal state while avoiding unsafe states with certain probabilistic guarantees. Therefore the underlying Markov chain for any control policy will be Multichain or non-ergodic since by definition there exists a goal set and an unsafe set. Bellman's principle of optimality does not hold for such a safety-constrained Markov decision process in a Multichain setting as highlighted by a counterexample. We resolve the aforementioned counterexample by considering a zero-sum game setting between the policy and the Lagrange multiplier vector. Under suitable assumptions regarding the existence of admissible policy, we propose an off-policy RL algorithm for learning an optimal policy that satisfies the probabilistic safety guarantees. After that, we present the finite time error bound of the proposed RL algorithm. Lastly, we present simulation results of the aforementioned RL algorithm on a robot in a grid world setting.