Journal articles on the topic 'Players' learning'
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Bojanić, Milana, and Goran Bojanić. "Self-Learning Mechanism for Mobile Game Adjustment towards a Player." Applied Sciences 11, no. 10 (May 13, 2021): 4412. http://dx.doi.org/10.3390/app11104412.
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Mobile app markets have faced huge expansion during the last decade. Among different apps, games represent a large portion with a wide range of game categories having consumers in all age groups. To make a mobile game suitable for different age categories, it is necessary to adjust difficulty levels in such a way to keep the game challenging for different players with different playing skills. The mobile app puzzle game Wonderful Animals has been developed consisting of puzzles, find pairs and find differences game (available on the Google Play Store). The game testing was conducted on a group of 40 players by recording game level completion time and conducting a survey of their subjective evaluation of completed level difficulty. The study aimed to find a mechanism to adjust game level difficulty to the individual player taking into account the player’s achievements on previously played games. A pseudo-algorithm for self-learning mechanism is presented, enabling level difficulty adaptation to the player. Furthermore, player classification into three classes using neural networks is suggested in order to offer a user-specific playing environment. The experimental results show that the average recognition rate of the player class was 96.1%.
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Wu, Tianhang, Hanchen Wang, Jian Yang, Liang Xu, Yumeng Li, and Jun Zhang. "The prisoner’s dilemma game on scale-free networks with heterogeneous imitation capability." International Journal of Modern Physics C 29, no. 09 (September 2018): 1850077. http://dx.doi.org/10.1142/s0129183118500778.
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In human societies, personal heterogeneity may affect the strategy adoption capability of the individuals. In this paper, we study the effects of heterogeneous learning ability on the evolution of cooperation by introducing heterogeneous imitation capability of players. We design a pre-factor [Formula: see text] to represent the heterogeneous learning ability of players, which is related to the degree of players. And a parameter [Formula: see text] is used to tune the learning levels. If [Formula: see text], the learning ability of players decreases and the low-degree player has the higher reduction level, but if [Formula: see text], the learning ability of low-degree players enhances to a higher level. By carrying out extensive simulations, it reveals that the evolution of cooperation is influenced significantly by introducing player’s heterogeneous learning ability and can be promoted under the right circumstances. This finding sheds some light on the important effect of individual heterogeneity on the evolutionary game.
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Wolitzky, Alexander. "Learning from Others' Outcomes." American Economic Review 108, no. 10 (October 1, 2018): 2763–801. http://dx.doi.org/10.1257/aer.20170914.
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I develop a simple model of social learning in which players observe others’ outcomes but not their actions. A continuum of players arrives continuously over time, and each player chooses once-and-for-all between a safe action (which succeeds with known probability) and a risky action (which succeeds with fixed but unknown probability, depending on the state of the world). The actions also differ in their costs. Before choosing, a player observes the outcomes of K earlier players. There is always an equilibrium in which success is more likely in the good state, and this alignment property holds whenever the initial generation of players is not well informed about the state. In the case of an outcome-improving innovation (where the risky action may yield a higher probability of success), players take the correct action as K → ∞. In the case of a cost-saving innovation (where the risky action involves saving a cost but accepting a lower probability of success), inefficiency persists as K → ∞ in any aligned equilibrium. Whether inefficiency takes the form of under-adoption or over-adoption also depends on the nature of the innovation. Convergence of the population to equilibrium may be nonmonotone. (JEL D81, D83, O32, Q12, Q16)
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Silva, João Vítor Rocha da, and Paulo Canas Rodrigues. "All-NBA Teams’ Selection Based on Unsupervised Learning." Stats 5, no. 1 (February 9, 2022): 154–71. http://dx.doi.org/10.3390/stats5010011.
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All-NBA Teams’ selections have great implications for the players’ and teams’ futures. Since contract extensions are highly related to awards, which can be seen as indexes that measure a players’ production in a year, team selection is of mutual interest for athletes and franchises. In this paper, we are interested in studying the current selection format. In particular, this study aims to: (i) identify the factors that are taken into consideration by voters when choosing the three All-NBA Teams; and (ii) suggest a new selection format to evaluate players’ performances. Average game-related statistics of all active NBA players in regular seasons from 2013-14 to 2018-19, were analyzed using LASSO (Logistic) Regression and Principal Component Analysis (PCA). It was possible: (i) to determine an All-NBA player profile; (ii) to determine that this profile can cause a misrepresentation of players’ modern and versatile gameplay styles; and (iii) to suggest a new way to evaluate and select players, through PCA. As the results of this paper a model is presented that may help not only the NBA to better evaluate players, but any basketball league; it also may be a source to researchers that aim to investigate player performance, development, and their impact over many seasons.
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Hurkens, Sjaak. "Learning by Forgetful Players." Games and Economic Behavior 11, no. 2 (November 1995): 304–29. http://dx.doi.org/10.1006/game.1995.1053.
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Li, Neill Y., Nicholas J. Lemme, Steven F. Defroda, Elvis Nunez, Davis A. Hartnett, and Brett D. Owens. "Performance After Operative Versus Nonoperative Management of Shoulder Instability in the National Basketball Association." Orthopaedic Journal of Sports Medicine 7, no. 12 (December 1, 2019): 232596711988933. http://dx.doi.org/10.1177/2325967119889331.
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Background: Although nonoperative management after shoulder instability injury allows an athlete to return to play sooner than operative intervention, higher rates of recurrence have been observed after nonoperative management. However, no study has investigated the differences in performance of National Basketball Association (NBA) players after index shoulder instability events managed nonoperatively versus operatively. Purpose/Hypothesis: The purpose of this study was to identify shoulder instability events in NBA athletes and assess differences in performance after injury with nonoperative versus operative management. We hypothesized that players who undergo operative intervention have reduced risk of recurrence and are able to continue their elite level of play as opposed to those who undergo nonoperative management. Study Design: Cohort study; Level of evidence, 3. Methods: Publicly available injury data from the 1986-1987 through 2017-2018 seasons were reviewed to identify NBA athletes sustaining a shoulder instability event. In addition to characteristics, player performance information, including games played, player efficiency rating (PER), and win shares, was analyzed before and for 3 seasons after injury. Statistical learning models were applied to identify performance variables that have the greatest predictive value to determine players who would benefit from surgery. Results: A total of 60 players with shoulder instability events were identified between 1986 and 2018. After injury, 37 players (61.7%) eventually underwent surgery and 23 players (38.3%) did not. Players who were treated nonoperatively had significantly decreased PER, games played, and offensive win shares in the season after injury ( P < .05). Players who underwent surgery did not see a decline in PER, games played, or win shares. Random forest modeling found that true shooting percentage and win shares per 48 minutes were the performance variables most predictive in determining which players would benefit from surgery after shoulder instability. Conclusion: Players who underwent surgical intervention for shoulder instability maintained their PER, games played, and win share performance characteristics, whereas players who did not undergo surgery had declines in these parameters. Given the demands of shoulder function in basketball and the risk of recurrence after an instability event, surgery enhances a player’s opportunity to maintain a high level of performance after injury.
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Santillan, Luis Alberto Casillas, and Johor Ismael Jara Gonzalez. "Learning Avatar's Locomotion Patterns Through Spatial Analysis in FPS Video Games." International Journal of Organizational and Collective Intelligence 8, no. 1 (January 2018): 28–45. http://dx.doi.org/10.4018/ijoci.2018010103.
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This article describes how current video games offer an extreme use of media fusion. Such construction implies a novel form of complexity regarding game control and active response from game to player. All of these elements produce deeper immersion effect in players. In order to perform a detailed supervision over this kind of game, additional controls should be included in game. Some of these controls are the moving and decision schemes. Authors believe that players move around virtual scenarios following some sort of pattern. Every player would have a specific pattern, according to his/her experience and capability to manage the gamepad layout. Current proposal consists in a 3D geometrical model surrounding player's avatar. Data unwittingly provided by the player, have elements to discover and, eventually, learn some gamers' patterns. The availability of these patterns would allow an improved game response and even the possibility of machine learning, as well as other artificial intelligence strategies. Every 3D game may include the model proposed in this paper, due to its noninvasive operation.
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Karnuta, Jaret M., Bryan C. Luu, Heather S. Haeberle, Paul M. Saluan, Salvatore J. Frangiamore, Kim L. Stearns, Lutul D. Farrow, et al. "Machine Learning Outperforms Regression Analysis to Predict Next-Season Major League Baseball Player Injuries: Epidemiology and Validation of 13,982 Player-Years From Performance and Injury Profile Trends, 2000-2017." Orthopaedic Journal of Sports Medicine 8, no. 11 (November 1, 2020): 232596712096304. http://dx.doi.org/10.1177/2325967120963046.
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Background: Machine learning (ML) allows for the development of a predictive algorithm capable of imbibing historical data on a Major League Baseball (MLB) player to accurately project the player's future availability. Purpose: To determine the validity of an ML model in predicting the next-season injury risk and anatomic injury location for both position players and pitchers in the MLB. Study Design: Descriptive epidemiology study. Methods: Using 4 online baseball databases, we compiled MLB player data, including age, performance metrics, and injury history. A total of 84 ML algorithms were developed. The output of each algorithm reported whether the player would sustain an injury the following season as well as the injury’s anatomic site. The area under the receiver operating characteristic curve (AUC) primarily determined validation. Results: Player data were generated from 1931 position players and 1245 pitchers, with a mean follow-up of 4.40 years (13,982 player-years) between the years of 2000 and 2017. Injured players spent a total of 108,656 days on the disabled list, with a mean of 34.21 total days per player. The mean AUC for predicting next-season injuries was 0.76 among position players and 0.65 among pitchers using the top 3 ensemble classification. Back injuries had the highest AUC among both position players and pitchers, at 0.73. Advanced ML models outperformed logistic regression in 13 of 14 cases. Conclusion: Advanced ML models generally outperformed logistic regression and demonstrated fair capability in predicting publicly reportable next-season injuries, including the anatomic region for position players, although not for pitchers.
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Hernandez, Jérôme, Mathieu Muratet, Matthis Pierotti, and Thibault Carron. "Can We Detect Non-playable Characters’ Personalities Using Machine And Deep Learning Approaches?" European Conference on Games Based Learning 16, no. 1 (September 29, 2022): 271–79. http://dx.doi.org/10.34190/ecgbl.16.1.627.
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Personality recognition and computational psychometrics data have become prevalent in personnel selection processes. Such assessment tools are adequate for human resources seeking tools to assess a large volume of diverse player personalities in the current "war of talents." Recently, studies about using Gamified situational judgment test approaches have shown positive results in assessing players' behavior and personality.
Gamified situational judgment tests combine the advantages of gamification, such as enhancing players' reactions and flow state, with the acknowledged traditional situational judgment test approach. To gamify a situational judgment test, an innovative approach using the visual novel game genre has shown positive results in the gamification by adding game elements such as narrative scripts, non-player characters, dialogs, and audiovisual assets to the test. Indeed, these elements play an essential role in the validity of the players' personality results by using a stealth-assessment method to minimize social bias and player's stress. However, to our knowledge, as gamification in personality detection is still recent, little is known on the possible positive outcomes of designing game elements such as the dialogues and non-player character personalities in the validity of the team cohesion measure. To this end, we propose an empirical study to build personality trait models based on non-players characters' speeches.
We used the Myers–Briggs Type Indicator based on four dichotomies to classify the personalities as one of companies and organizations' most used personality typology. For each of the four dimensions, we train twenty-four separate binary classifiers and one 16-class classifier, using well-established machine learning and a convolutional neural network in the domain of natural language processing, text analytics, and computational psychometrics. The results of this study show that it is possible to recognize non-playable characters’ personalities and thus can help game designers to understand their characters' personalities using natural language processing.
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Stanley, Kenneth, Ryan Cornelius, Risto Miikkulainen, Thomas D’Silva, and Aliza Gold. "Real-Time Learning in the NERO Video Game." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 1, no. 1 (September 28, 2021): 159–60. http://dx.doi.org/10.1609/aiide.v1i1.18736.
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If game characters could learn through interacting with the player, behavior could improve as the game is played, keeping it interesting. The real-time NeuroEvolution of Augmenting Topologies (rtNEAT) method, which can evolve increasingly complex artificial neural networks in real time as a game is being played, will be presented. The rtNEAT method makes possible an entirely new genre of video games in which the player trains a team of agents through a series of customized exercises. In order to demonstrate this concept, the NeuroEvolving Robotic Operatives (NERO) game was built based on rtNEAT. In NERO, the player trains a team of virtual robots for combat against other players' teams. The live demo will show how agents in NERO adapt in real time as they interact with the player. In the future, rtNEAT may allow new kinds of educational and training applications through interactive and adapting games.
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Tastan, Bulent, and Gita Sukthankar. "Learning Policies for First Person Shooter Games Using Inverse Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 7, no. 1 (October 9, 2011): 85–90. http://dx.doi.org/10.1609/aiide.v7i1.12430.
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The creation of effective autonomous agents (bots) for combat scenarios has long been a goal of the gaming industry. However, a secondary consideration is whether the autonomous bots behave like human players; this is especially important for simulation/training applications which aim to instruct participants in real-world tasks. Bots often compensate for a lack of combat acumen with advantages such as accurate targeting, predefined navigational networks, and perfect world knowledge, which makes them challenging but often predictable opponents. In this paper, we examine the problem of teaching a bot to play like a human in first-person shooter game combat scenarios. Our bot learns attack, exploration and targeting policies from data collected from expert human player demonstrations in Unreal Tournament. We hypothesize that one key difference between human players and autonomous bots lies in the relative valuation of game states. To capture the internal model used by expert human players to evaluate the benefits of different actions, we use inverse reinforcement learning to learn rewards for different game states. We report the results of a human subjects' study evaluating the performance of bot policies learned from human demonstration against a set of standard bot policies. Our study reveals that human players found our bots to be significantly more human-like than the standard bots during play. Our technique represents a promising stepping-stone toward addressing challenges such as the Bot Turing Test (the CIG Bot 2K Competition).
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Krichene, Walid, Mohamed Chedhli Bourguiba, Kiet Tlam, and Alexandre Bayen. "On Learning How Players Learn." ACM Transactions on Cyber-Physical Systems 2, no. 1 (February 23, 2018): 1–23. http://dx.doi.org/10.1145/3078620.
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Wang, Tianyi, and Tongyan Li. "Deep Learning-Based Football Player Detection in Videos." Computational Intelligence and Neuroscience 2022 (July 12, 2022): 1–8. http://dx.doi.org/10.1155/2022/3540642.
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The main task of football video analysis is to detect and track players. In this work, we propose a deep convolutional neural network-based football video analysis algorithm. This algorithm aims to detect the football player in real time. First, five convolution blocks were used to extract a feature map of football players with different spatial resolution. Then, features from different levels are combined together with weighted parameters to improve detection accuracy and adapt the model to input images with various resolutions and qualities. Moreover, this algorithm can be extended to a framework for detecting players in any other sports. The experimental results assure the effectiveness of our algorithm.
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Syufagi, Moh Aries, Mochamad Hariadi, and Mauridhi Hery Purnomo. "Petri Net Model for Serious Games Based on Motivation Behavior Classification." International Journal of Computer Games Technology 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/851287.
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Petri nets are graphical and mathematical tool for modeling, analyzing, and designing discrete event applicable to many systems. They can be applied to game design too, especially to design serous game. This paper describes an alternative approach to the modeling of serious game systems and classification of motivation behavior with Petri nets. To assess the motivation level of player ability, this research aims at Motivation Behavior Game (MBG). MBG improves this motivation concept to monitor how players interact with the game. This modeling employs Learning Vector Quantization (LVQ) for optimizing the motivation behavior input classification of the player. MBG may provide information when a player needs help or when he wants a formidable challenge. The game will provide the appropriate tasks according to players’ ability. MBG will help balance the emotions of players, so players do not get bored and frustrated. Players have a high interest to finish the game if the players are emotionally stable. Interest of the players strongly supports the procedural learning in a serious game.
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Tian, Changjia, Varuna De Silva, Michael Caine, and Steve Swanson. "Use of Machine Learning to Automate the Identification of Basketball Strategies Using Whole Team Player Tracking Data." Applied Sciences 10, no. 1 (December 18, 2019): 24. http://dx.doi.org/10.3390/app10010024.
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The use of machine learning to identify and classify offensive and defensive strategies in team sports through spatio-temporal tracking data has received significant interest recently in the literature and the global sport industry. This paper focuses on data-driven defensive strategy learning in basketball. Most research to date on basketball strategy learning has focused on offensive effectiveness and is based on the interaction between the on-ball player and principle on-ball defender, thereby ignoring the contribution of the remaining players. Furthermore, most sports analytical systems that provide play-by-play data is heavily biased towards offensive metrics such as passes, dribbles, and shots. The aim of the current study was to use machine learning to classify the different defensive strategies basketball players adopt when deviating from their initial defensive action. An analytical model was developed to recognise the one-on-one (matched) relationships of the players, which is utilised to automatically identify any change of defensive strategy. A classification model is developed based on a player and ball tracking dataset from National Basketball Association (NBA) game play to classify the adopted defensive strategy against pick-and-roll play. The methodology described is the first to analyse the defensive strategy of all in-game players (both on-ball players and off-ball players). The cross-validation results indicate that the proposed technique for automatic defensive strategy identification can achieve up to 69% accuracy of classification. Machine learning techniques, such as the one adopted here, have the potential to enable a deeper understanding of player decision making and defensive game strategies in basketball and other sports, by leveraging the player and ball tracking data.
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Goslen, Alex, Dan Carpenter, Jonathan Rowe, Roger Azevedo, and James Lester. "Robust Player Plan Recognition in Digital Games with Multi-Task Multi-Label Learning." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 18, no. 1 (October 11, 2022): 105–12. http://dx.doi.org/10.1609/aiide.v18i1.21953.
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Plan recognition is a key component of player modeling. Player plan recognition focuses on modeling how and when players select goals and formulate action sequences to achieve their goals during gameplay. By occasionally asking players to describe their plans, it is possible to devise robust plan recognition models that jointly reason about player goals and action sequences in coordination with player input. In this work, we present a player plan recognition framework that leverages data from player interactions with a planning support tool embedded in an educational game for middle school science education, CRYSTAL ISLAND. Players are prompted to use the planning tool to describe their goals and planned actions in CRYSTAL ISLAND. We use this data to devise data-driven player plan recognition models using multi-label multi-task learning. Specifically, we compare single-task and multi-task learning approaches for both goal prediction and action sequence prediction. Results indicate that multi-task learning yields significant benefits for action sequence prediction. Additionally, we find that incorporating automated detectors of plan completion in plan recognition models improves predictive performance in both tasks.
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Lin, Zhiyu, Kyle Xiao, and Mark Riedl. "GenerationMania: Learning to Semantically Choreograph." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 15, no. 1 (October 8, 2019): 52–58. http://dx.doi.org/10.1609/aiide.v15i1.5224.
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Beatmania is a rhythm action game where players must reproduce some of the sounds of a song by pressing specific controller buttons at the correct time. In this paper we investigate the use of deep neural networks to automatically create game stages—called charts—for arbitrary pieces of music. Our technique uses a multi-layer feed-forward network trained on sound sequence summary statistics to predict which sounds in the music are to be played by the player and which will play automatically. We use another neural network along with rules to determine which controls should be mapped to which sounds. We evaluated our system on the ability to reconstruct charts in a held-out test set, achieving an F1-score that significantly beats LSTM baselines.
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Leoni, Patrick L. "Learning in General Games with Nature’s Moves." Journal of Applied Mathematics 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/453168.
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This paper investigates simultaneous learning about both nature and others’ actions in repeated games and identifies a set of sufficient conditions for which Harsanyi’s doctrine holds. Players have a utility function over infinite histories that are continuous for the sup-norm topology. Nature’s drawing after any history may depend on any past actions. Provided that (1) every player maximizes her expected payoff against her own beliefs, (2) every player updates her beliefs in a Bayesian manner, (3) prior beliefs about both nature and other players’ strategies have a grain of truth, and (4) beliefs about nature are independent of actions chosen during the game, we construct a Nash equilibrium, that is, realization-equivalent to the actual plays, where Harsanyi’s doctrine holds. Those assumptions are shown to be tight.
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Kubo, Masao, Sadayoshi Mikami, Yukinori Kakazu, and Mitsuo Wada. "Adaptive Formation Plays in Simulated Soccer Game Based on Pheromon as Communication Media and Reward Resources." Journal of Robotics and Mechatronics 11, no. 1 (February 20, 1999): 72–77. http://dx.doi.org/10.20965/jrm.1999.p0072.
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In this paper, a methodology for self-organization of autonomous agents is proposed. Agents make an organization to adapt their environment, building an adaptable soccer team. In soccer games, each player must forecast correctly, and players behave strongly as a team. They must also react and reform team plays if accidents happen by statistical movement of the ball. To adapt the soccer environment, we introduce an on-time communication style among autonomous agents, pheromone communication. Agents as players give their forecasting (represented by pheromones) acquired by the learning ability of each agent. Using by forecasting, they discuss and reform team play. With this learning ability in forecasting by each player, we expect they will make a close soccer team. When they start learning, they cannot behave sophisticatedly but, after sufficient learning trials, they behave like offensive professional players.
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Mustač, Kuzma, Krešimir Bačić, Lea Skorin-Kapov, and Mirko Sužnjević. "Predicting Player Churn of a Free-to-Play Mobile Video Game Using Supervised Machine Learning." Applied Sciences 12, no. 6 (March 9, 2022): 2795. http://dx.doi.org/10.3390/app12062795.
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Free-to-play mobile games monetize players through different business models, with higher player engagement leading to revenue increases. Consequently, the foremost goal of game designers and developers is to keep their audience engaged with the game for as long as possible. Studying and modeling player churn is, therefore, of the highest importance for game providers in this genre. This paper presents machine learning-based models for predicting player churn in a free-to-play mobile game. The dataset on which the research is based is collected in cooperation with a European game developer and comprises over four years of player records of a game belonging to the multiple-choice storytelling genre. Our initial analysis shows that user churn is a very significant problem, with a large portion of the players engaging with the game only briefly, thus presenting a potentially huge revenue loss. Presented models for churn prediction are trained based on varying learning periods (1–7 days) to encompass both very short-term players and longer-term players. Further, the predicted churn periods vary from 1–7 days. Obtained results show accuracies varying from 66% to 95%, depending on the considered periods.
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De, Shipra, and Darryl A. Seale. "Dynamic Decision Making and Race Games." ISRN Operations Research 2013 (August 7, 2013): 1–15. http://dx.doi.org/10.1155/2013/452162.
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Frequent criticism of dynamic decision making research pertains to the overly complex nature of the decision tasks used in experimentation. To address such concerns, we study dynamic decision making with respect to a simple race game, which has a computable optimal strategy. In this two-player race game, individuals compete to be the first to reach a designated threshold of points. Players alternate rolling a desired quantity of dice. If the number one appears on any of the dice, the player receives no points for his turn; otherwise, the sum of the numbers appearing on the dice is added to the player's score. Results indicate that although players are influenced by the game state when making their decisions, they tend to play too conservatively in comparison to the optimal policy and are influenced by the behavior of their opponents. Improvement in performance was negligible with repeated play. Survey data suggests that this outcome could be due to inadequate time for learning or insufficient player motivation. However, some players approached optimal heuristic strategies, which perform remarkably well.
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Leuchter, Inbal, and Gila Kurtz. "Effects of Instructions, Assistance, Narrative, Competition, Challenge, and Age on Performances in Digital Learning Games." International Journal of Advanced Corporate Learning (iJAC) 15, no. 2 (November 29, 2022): 16–33. http://dx.doi.org/10.3991/ijac.v15i2.30867.
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Studies have shown that digital game-based learning (DGBL) can stimulate learners and increase motivation. However, in order to accomplish these goals, we must understand the role and impact of the game elements. This study aimed to examine the effects of four-game elements on player performance: Instructions and assistance, narrative, Competition, and Challenge. An additional factor examined was players' age. The data was collected using BIG DATA from the game platform, which recorded the scores of 3,281 users across nine different games, during the period 2015-2020. Users played as part of their visit to 'Musa', a multidisciplinary museum of local cultural materials in Tel-Aviv, either in 'Family' game mode or in 'Group' mode. According to the results of our study, players performed better on 'Group' games. In addition, players' performances improved when narrative depth was significant and the play area was smaller. Separating our data into two groups led to additional results: players in 'Family' mode performed better when the game instructions included a video, while in 'Group' modes participants performed better when a human guide was available to some extent. The results of this study and their implications can assist educators and game designers in planning more accurate and effective learning games.
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Silva, Agustin, Omar Gustavo Zabaleta, and Constancio Miguel Arizmendi. "Learning Mixed Strategies in Quantum Games with Imperfect Information." Quantum Reports 4, no. 4 (October 29, 2022): 462–75. http://dx.doi.org/10.3390/quantum4040033.
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The quantization of games expand the players strategy space, allowing the emergence of more equilibriums. However, finding these equilibriums is difficult, especially if players are allowed to use mixed strategies. The size of the exploration space expands so much for quantum games that makes far harder to find the player’s best strategy. In this work, we propose a method to learn and visualize mixed quantum strategies and compare them with their classical counterpart. In our model, players do not know in advance which game they are playing (pay-off matrix) neither the action selected nor the reward obtained by their competitors at each step, they only learn from an individual feedback reward signal. In addition, we study both the influence of entanglement and noise on the performance of various quantum games.
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Chassang, Sylvain. "Building Routines: Learning, Cooperation, and the Dynamics of Incomplete Relational Contracts." American Economic Review 100, no. 1 (March 1, 2010): 448–65. http://dx.doi.org/10.1257/aer.100.1.448.
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This paper studies how agents with conflicting interests learn to cooperate when the details of cooperation are not common knowledge. It considers a repeated game in which one player has incomplete information about when and how her partner can provide benefits. Initially, monitoring is imperfect and cooperation requires inefficient punishment. As the players' common history grows, the uninformed player can learn to monitor her partner's actions, which allows players to establish more efficient cooperative routines. Because revealing information is costly, it may be optimal not to reveal all the existing information, and efficient equilibria can be path-dependent. (JEL C73, D82, D83, D86)
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Cope, Peter, and Hugh Smith. "Cultural context in musical instrument learning." British Journal of Music Education 14, no. 3 (November 1997): 283–89. http://dx.doi.org/10.1017/s026505170000125x.
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The importance of a cultural context for musical instrument teaching and learning is analysed in terms of situated cognition and cultural validity. It is suggested that the current cultural location of instrument teaching is often associated with a view that confines success to a minority of children, partly by retaining the notion of the concert player as the goal. The nature of this goal and its implications are discussed and compared with traditional instrument learning and playing. Given that recent research suggests that virtuoso players are the product of practice rather than innate talent, the authors argue that a more relevant cultural framework for instrument teaching would result in competent players whose facility with an instrument would be appropriate to their social context.
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Mizrahi, Dor, Inon Zuckerman, and Ilan Laufer. "Level-K Classification from EEG Signals Using Transfer Learning." Sensors 21, no. 23 (November 27, 2021): 7908. http://dx.doi.org/10.3390/s21237908.
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Tacit coordination games are games in which communication between the players is not allowed or not possible. In these games, the more salient solutions, that are often perceived as more prominent, are referred to as focal points. The level-k model states that players’ decisions in tacit coordination games are a consequence of applying different decision rules at different depths of reasoning (level-k). A player at Lk=0 will randomly pick a solution, whereas a Lk≥1 player will apply their strategy based on their beliefs regarding the actions of the other players. The goal of this study was to examine, for the first time, the neural correlates of different reasoning levels in tacit coordination games. To that end, we have designed a combined behavioral-electrophysiological study with 3 different conditions, each resembling a different depth reasoning state: (1) resting state, (2) picking, and (3) coordination. By utilizing transfer learning and deep learning, we were able to achieve a precision of almost 100% (99.49%) for the resting-state condition, while for the picking and coordination conditions, the precision was 69.53% and 72.44%, respectively. The application of these findings and related future research options are discussed.
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Mengel, Friederike. "Learning by (limited) forward looking players." Journal of Economic Behavior & Organization 108 (December 2014): 59–77. http://dx.doi.org/10.1016/j.jebo.2014.08.001.
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Horn, Britton, Josh Miller, Gillian Smith, and Seth Cooper. "A Monte Carlo Approach to Skill-Based Automated Playtesting." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 14, no. 1 (September 25, 2018): 166–72. http://dx.doi.org/10.1609/aiide.v14i1.13036.
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In order to create well-crafted learning progressions, designers guide players as they present game skills and give ample time for the player to master those skills. However, analyzing the quality of learning progressions is challenging, especially during the design phase, as content is ever-changing. This research presents the application of Stratabots — automated player simulations based on models of players with varying sets of skills — to the human computation game Foldit. Stratabot performance analysis coupled with player data reveals a relatively smooth learning progression within tutorial levels, yet still shows evidence for improvement. Leveraging existing general gameplaying algorithms such as Monte Carlo Evaluation can reduce the development time of this approach to automated playtesting without losing predicitive power of the player model.
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Tsaur, Woei-Jiunn, Chinyang Henry Tseng, and Chin-Ling Chen. "Effective Bots’ Detection for Online Smartphone Game Using Multilayer Perceptron Neural Networks." Security and Communication Networks 2022 (March 29, 2022): 1–10. http://dx.doi.org/10.1155/2022/9429475.
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Online smartphone game bots can cause unfair behaviors and even shorten the game’s life cycle. The random forest algorithm in machine learning is a widely used solution to identify game bots through behavioral features. Although the random forest algorithm can exactly detect more definite game bot players, some players belonging to the gray area cannot be detected accurately. Therefore, this study collects players’ data and extracts the features to build the multilayer perceptron, neural network model, for effectively detecting online smartphone game bots. This approach calculates each player’s abnormal rate to judge game bots and is evaluated on the famous mobile online game. Based on these abnormal rates, we then use K means to cluster players and further define the gray area. In the experimental evaluation, the results demonstrate the proposed learning model has better performance, not only increasing the accuracy but also reducing the error rate as compared with the random forest model in the same players’ dataset. Accordingly, the proposed learning model can detect bot players more accurately and is feasible for real online smartphone games.
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Mora, Antonio Miguel, Francisco Aisa, Pablo García-Sánchez, Pedro Ángel Castillo, and Juan Julián Merelo. "Modelling a Human-Like Bot in a First Person Shooter Game." International Journal of Creative Interfaces and Computer Graphics 6, no. 1 (January 2015): 21–37. http://dx.doi.org/10.4018/ijcicg.2015010102.
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Autonomous agents in videogames, usually called bots, have tried to behave as human players from their emergence more than 20 years ago. They normally try to model a part of a human expert player's knowledge with respect to the game, trying to become a competitive opponent or a good partner for other players. This paper presents a deep description of the design of a bot for playing 1 vs. 1 Death Match mode in the first person shooter Unreal Tournament™ 2004 (UT2K4). This bot uses a state-based Artificial Intelligence model which emulates a big part of the behavior/knowledge (actions and tricks) of an expert human player in this mode. This player has participated in international UT2K4 championships. The behavioral engine considers primary and secondary actions, and uses a memory approach. It is based in an auxiliary database for learning about the fighting arena, so it stores weapons and items locations once the bot has discovered them, as a human player would do. This so-called Expert Bot has yielded excellent results, beating the game default bots even in the hardest difficulty, and being a very hard opponent for medium-level human players.
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Bunian, Sara, Alessandro Canossa, Randy Colvin, and Magy Seif El-Nasr. "Modeling Individual Differences in Game Behavior Using HMM." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 13, no. 1 (June 25, 2021): 158–64. http://dx.doi.org/10.1609/aiide.v13i1.12942.
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Player modeling is an important concept that has gained much attention in game research due to its utility in developing adaptive techniques to target better designs for engagement and retention. Previous work has explored modeling individual differences using machine learning algorithms performed on aggregated game actions. However, players’ individual differences may be better manifested through sequential patterns of the in-game player’s actions. While few works have explored sequential analysis of player data, none have explored the use of Hidden Markov Models (HMM) to model individual differences, which is the topic of this paper. In particular, we developed a modeling approach using data collected from players playing a Role-Playing Game (RPG). Our proposed approach is two fold: 1. We present a Hidden Markov Model (HMM) of player in-game behaviors to model individual differences, and 2. using the output of the HMM, we generate behavioral features used to classify real world players’ characteristics, including game expertise and the big five personality traits. Our results show predictive power for some of personality traits, such as game expertise and conscientiousness, but the most influential factor was game expertise.
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Gupta, Anisha, Dan Carpenter, Wookhee Min, Jonathan Rowe, Roger Azevedo, and James Lester. "Enhancing Multimodal Goal Recognition in Open-World Games with Natural Language Player Reflections." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 18, no. 1 (October 11, 2022): 37–44. http://dx.doi.org/10.1609/aiide.v18i1.21945.
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Open-world games promote engagement by offering players a high degree of autonomy to explore expansive game worlds. Player goal recognition has been widely explored for modeling player behavior in open-world games by dynamically recognizing players’ goals using observations of in-game actions and locations. In educational open-world games, in-game reflection tools can help students reflect on their learning and plan their strategies for future gameplay. Data generated from students’ written reflections can serve as a source of evidence for modeling player goals. We present a multimodal goal recognition approach that leverages players’ written reflections along with game trace log features to predict player goals during gameplay. Results show that both the highest predictive performance and best early prediction performance are achieved by deep learning-based, multimodal goal recognition models that utilize both written reflection and gameplay features as input. These models outperform unimodal deep learning models as well as a random forest baseline. Multimodal goal recognition using natural language reflection data has significant potential to enhance goal recognition model performance, as well as player modeling more generally, to support the creation of engaging and adaptive open-world digital games.
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Maare, Åsa Harvard. "Playing cards: spatial arrangements for observational learning." Psychology of Language and Communication 22, no. 1 (January 1, 2018): 187–97. http://dx.doi.org/10.2478/plc-2018-0008.
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Abstract This paper looks at how players of a card game create spatial arrangements of playing cards, and the cognitive and communicative effects of such arrangements. The data is an episode of two 8-year old children and a teacher playing the combinatorial card game Set, in the setting of the leisure-time center. The paper explores and explains how the visual resources of the game are used for externalizing information in terms of distributed cognition and epistemic actions. The paper also examines how other participants attend to the visual arrangements and self-directed talk of the active player. The argument is that externalizing information may be a strategy for reducing cognitive load for the individual problem-solver, but it is also a communicative behaviour affecting other participants and causing them to engage with the problem and the problem-solver. Seeing and hearing players who have succeeded in finding a set provide observers with rich learning opportunities, and increases their motivation to play the game. From the point of view of learning design, the consequence of this is that bystanders merit to be considered as the potential learners of a pedagogical game as much as the players themselves
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Donahue, Kate, and Jon Kleinberg. "Model-sharing Games: Analyzing Federated Learning Under Voluntary Participation." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 6 (May 18, 2021): 5303–11. http://dx.doi.org/10.1609/aaai.v35i6.16669.
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Federated learning is a setting where agents, each with access to their own data source, combine models learned from local data to create a global model. If agents are drawing their data from different distributions, though, federated learning might produce a biased global model that is not optimal for each agent. This means that agents face a fundamental question: should they join the global model or stay with their local model? In this work, we show how this situation can be naturally analyzed through the framework of coalitional game theory. Motivated by these considerations, we propose the following game: there are heterogeneous players with different model parameters governing their data distribution and different amounts of data they have noisily drawn from their own distribution. Each player's goal is to obtain a model with minimal expected mean squared error (MSE) on their own distribution. They have a choice of fitting a model based solely on their own data, or combining their learned parameters with those of some subset of the other players. Combining models reduces the variance component of their error through access to more data, but increases the bias because of the heterogeneity of distributions. In this work, we derive exact expected MSE values for problems in linear regression and mean estimation. We use these values to analyze the resulting game in the framework of hedonic game theory; we study how players might divide into coalitions, where each set of players within a coalition jointly constructs a single model. In a case with arbitrarily many players that each have either a "small" or "large" amount of data, we constructively show that there always exists a stable partition of players into coalitions.
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Fairuzabadi, Ahmad, Ahmad Afif Supianto, and Herman Tolle. "Analysis of Players' Speed Thinking in Color Mix Game Application." International Journal of Interactive Mobile Technologies (iJIM) 12, no. 8 (December 24, 2018): 113. http://dx.doi.org/10.3991/ijim.v12i8.9279.
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<p class="0abstract">Color Mix Game is an educational game that introduces the theory of color mixing, which is very important for everyday needs, especially in all areas of visual art such as graphic design and videography. Unfortunately, just a few people know this knowledge. The game has become an interesting and fun learning media for its players. In addition to learning media, games can have positive impacts on the players. Among its effects, it can improve basic mental skills for players, such as alertness, speed thinking ability, and concentration ability. Therefore we are interested to analyze the player behavior in the game to see the improvement of their players' speed thinking ability. To perform this analysis, we collect the data by recording every player activity log during playing the game. Every step was taken by the player along with detailed information such as the time of the attack and the distance of the attack will be stored in the database. By using MySQL, the database of this game is built on PhpMyAdmin application. After collecting data from 10 respondents we asked to play this game 2 times, we get the result that the players experienced a speed increase of thinking as much as 32% in the game.</p>
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Duda, Henryk, and Aleksander Stuła. "Evaluate the level of creative support of the teaching process of football game tactics." Journal of Kinesiology and Exercise Sciences 27, no. 78 (June 30, 2017): 33–38. http://dx.doi.org/10.5604/01.3001.0011.6797.
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Teaching tactics in team games is a difficult process, because it requires not only the optimal preparation of the player in the physical and physical sphere but above all the deliberate action in the sport. Situations that arise when playing football, usually require the choice of one of several possible decisions. Not all decisions are optimal. It is on the basis of the optimal decision that you can judge the advantages of the player and then interpret his abilities. In order to rationally solve a task in the game, the player must have the best knowledge about the effective action, and they also need to use such tactical preparation methods so that the player can consciously and creatively participate in the training process. The purpose of research Determine whether modern football clubs are using tactical training based on creative support in teaching football tactics. Material and methods of testing The empirical material was collected by anonymous polls conducted among randomly selected football players in two age categories: junior (140 CLJ players and MW leagues) and senior (160 players IV and III leagues). Research in the years 2015 - 2017 was conducted in randomly selected Polish clubs, with the largest number (about 80%) being players in the Małopolskie, Świętokrzyskie, Podkarpackie, Silesian, Opole and Lubuskie voivodships. Results and conclusions The analysis of the research results shows that football players in the clubs surveyed are less likely to benefit from the knowledge transfer, which facilitates learning tactics. The reason for this situation is not only due to the limited organizational conditions for rational training but also can have its basis in the competence of trainers. This problem is significant for young players, which can limit not only the smooth operation of the game but also hinder the process of full development of the player. 1). Tactical training dominates traditional forms of instruction that limit the player's conscious participation in the game. 2). Among the trainers there is a low level of knowledge about the use of tactics to help teach the tactics of the game. 3) . In order to make more use of the teaching aids tactics, there is a need to include this learning direction in the training of trainers and football instructors. 4). Base conditions in football clubs limit the use of modern laboratory measures in teaching tactics.
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Schmid, Laura, Christian Hilbe, Krishnendu Chatterjee, and Martin A. Nowak. "Direct reciprocity between individuals that use different strategy spaces." PLOS Computational Biology 18, no. 6 (June 14, 2022): e1010149. http://dx.doi.org/10.1371/journal.pcbi.1010149.
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In repeated interactions, players can use strategies that respond to the outcome of previous rounds. Much of the existing literature on direct reciprocity assumes that all competing individuals use the same strategy space. Here, we study both learning and evolutionary dynamics of players that differ in the strategy space they explore. We focus on the infinitely repeated donation game and compare three natural strategy spaces: memory-1 strategies, which consider the last moves of both players, reactive strategies, which respond to the last move of the co-player, and unconditional strategies. These three strategy spaces differ in the memory capacity that is needed. We compute the long term average payoff that is achieved in a pairwise learning process. We find that smaller strategy spaces can dominate larger ones. For weak selection, unconditional players dominate both reactive and memory-1 players. For intermediate selection, reactive players dominate memory-1 players. Only for strong selection and low cost-to-benefit ratio, memory-1 players dominate the others. We observe that the supergame between strategy spaces can be a social dilemma: maximum payoff is achieved if both players explore a larger strategy space, but smaller strategy spaces dominate.
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McAvoy, Alex, and Martin A. Nowak. "Reactive learning strategies for iterated games." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 475, no. 2223 (March 2019): 20180819. http://dx.doi.org/10.1098/rspa.2018.0819.
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In an iterated game between two players, there is much interest in characterizing the set of feasible pay-offs for both players when one player uses a fixed strategy and the other player is free to switch. Such characterizations have led to extortionists, equalizers, partners and rivals. Most of those studies use memory-one strategies, which specify the probabilities to take actions depending on the outcome of the previous round. Here, we consider ‘reactive learning strategies’, which gradually modify their propensity to take certain actions based on past actions of the opponent. Every linear reactive learning strategy, p *, corresponds to a memory one-strategy, p , and vice versa. We prove that for evaluating the region of feasible pay-offs against a memory-one strategy, C ( p ) , we need to check its performance against at most 11 other strategies. Thus, C ( p ) is the convex hull in R 2 of at most 11 points. Furthermore, if p is a memory-one strategy, with feasible pay-off region C ( p ) , and p * is the corresponding reactive learning strategy, with feasible pay-off region C ( p ∗ ) , then C ( p ∗ ) is a subset of C ( p ) . Reactive learning strategies are therefore powerful tools in restricting the outcomes of iterated games.
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Dijkhuis, Talko B., Matthias Kempe, and Koen A. P. M. Lemmink. "Early Prediction of Physical Performance in Elite Soccer Matches—A Machine Learning Approach to Support Substitutions." Entropy 23, no. 8 (July 25, 2021): 952. http://dx.doi.org/10.3390/e23080952.
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Substitution is an essential tool for a coach to influence the match. Factors like the injury of a player, required tactical changes, or underperformance of a player initiates substitutions. This study aims to predict the physical performance of individual players in an early phase of the match to provide additional information to the coach for his decision on substitutions. Tracking data of individual players, except for goalkeepers, from 302 elite soccer matches of the Dutch ‘Eredivisie’ 2018–2019 season were used to enable the prediction of the individual physical performance. The players’ physical performance is expressed in the variables distance covered, distance in speed category, and energy expenditure in power category. The individualized normalized variables were used to build machine learning models that predict whether players will achieve 100%, 95%, or 90% of their average physical performance in a match. The tree-based algorithms Random Forest and Decision Tree were applied to build the models. A simple Naïve Bayes algorithm was used as the baseline model to support the superiority of the tree-based algorithms. The machine learning technique Random Forest combined with the variable energy expenditure in the power category was the most precise. The combination of Random Forest and energy expenditure in the power category resulted in precision in predicting performance and underperformance after 15 min in a match, and the values were 0.91, 0.88, and 0.92 for the thresholds 100%, 95%, and 90%, respectively. To conclude, it is possible to predict the physical performance of individual players in an early phase of the match. These findings offer opportunities to support coaches in making more informed decisions on player substitutions in elite soccer.
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Valls-Vargas, Josep, Santiago Ontañón, and Jichen Zhu. "Exploring Player Trace Segmentation for Dynamic Play Style Prediction." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 11, no. 1 (June 24, 2021): 93–99. http://dx.doi.org/10.1609/aiide.v11i1.12782.
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Existing work on player modeling often assumes that the play style of players is static. However, our recent work shows evidence that players regularly change their play style over time. In this paper we propose a novel player modeling framework to capture this change by using episodic information and sequential machine learning techniques. In particular, we experiment with different trace segmentation strategies for play style prediction. We evaluate this new framework on gameplay data gathered from a game-based interactive learning environment. Our results show that sequential machine learning techniques that incorporate predictions from previous segments outperform non-sequential techniques. Our results also show that too fine (minute-by-minute) or too coarse (whole trace) segmentation of traces decreases performance.
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Mitgutsch, Konstantin. "Playful Learning Experiences." International Journal of Gaming and Computer-Mediated Simulations 3, no. 3 (July 2011): 54–68. http://dx.doi.org/10.4018/jgcms.2011070104.
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Players use digital games as playgrounds for their interests, passions, values, and beliefs. Computer games entertain us, please our needs, challenge our abilities, make us engage with other players, and confront us with novel experiences. Today, video games foster learning, but how players connect their learning through playing games to their biographies is a question yet unanswered. This paper outlines basic theoretical assumptions on playful learning experiences and empirical insights into meaningful learning patterns. On this basis it presents the central results of an innovative qualitative study on playful learning biographies undertaken in 2010, and thereby aims to provide a reflected understanding of how today’s generation experiences deep and meaningful learning in their playful biographies. Furthermore, this paper examines the question on how games foster transformative learning and discusses consequences for educational settings and future research.
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Tjostheim, Ingvar, Vanessa Ayres-Pereira, Chris Wales, Angela Manna, and Simon Egenfeldt-Nielsen. "Dark Pattern: A Serious Game for Learning About the Dangers of Sharing Data." European Conference on Games Based Learning 16, no. 1 (September 29, 2022): 774–83. http://dx.doi.org/10.34190/ecgbl.16.1.872.
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Dark patterns refer to tricks built into websites and apps to manipulate users into acting unintentionally and detrimentally. An important issue is how such patterns might affect behaviour when actors are manoeuvred towards the sharing of their personal data, as exemplified in choices we face when downloading Apps or signing up for services provided on the internet. This paper presents our exploratory research into understanding the intention and subsequent actions of older teenagers responding to issues of personal data collection and (mis)use. The research is based on the competitive board-game Dark Pattern, in which players install apps, draw dark pattern cards, and make choices about the sharing of personal data. To win the game, a player must share as little data as possible and play cards that punish other players. We were interested to find out the extent to which the game was able to convey types of dark patterns to the players. Additionally, we wanted to explore how players’ perceptions of risks in data-sharing associated with their intention to protect their personal data. Finally, we were interested to explore potential gender difference, and whether this might be associated with intention to protect personal data. 56 of the students who played the game answered a subsequent survey with questions about their experiences and the data was analysed using Partial Least Squares – Structural Equation modelling (PLS-SEM). Despite the findings showing that playing the game had only limited impact on knowledge about dark patterns matters, the analysis of the relationship with the factors in our model shows that knowledge has a significant contribution on behavioural intention, demonstrating that students with high dark pattern knowledge also report higher intention to take steps to protect their data.
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Summerville, Adam, Matthew Guzdial, Michael Mateas, and Mark Riedl. "Learning Player Tailored Content From Observation: Platformer Level Generation from Video Traces using LSTMs." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 12, no. 2 (June 25, 2021): 107–13. http://dx.doi.org/10.1609/aiide.v12i2.12895.
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A touted use of Procedural Content Generation is generating content tailored to specific players. Previous work has relied on human identification of player profile features which are then mapped to level generator features. We present a machine-learned technique to train generators on Super Mario Bros. videos, generating levels based on latent play styles learned from the video. We evaluate the generators in comparison to the original levels and a machine-learned generator trained using simulated players.
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Zhang, Hong-Bin, and Hong Wang. "Group preferential selection promotes cooperation in spatial public goods game." International Journal of Modern Physics C 25, no. 11 (October 15, 2014): 1450062. http://dx.doi.org/10.1142/s0129183114500624.
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We study the evolution of cooperation in public goods games on the square lattice, focusing on the co-player learning mechanism based on the preferential selection that are brought about by wealthy information of groups where participants collect and search for potential imitators from those groups. We find that co-player learning mechanism based on the choice of weighted group can lead to the promotion of public cooperation by means of the information of wealthy groups that is obtained by participants, and after that the partial choice of public goods groups is enhanced with the tunable preferential parameter. Our results highlight that the learning interactions is not solely confined to the restricted connection among players, but co-players of wealthy groups have the opportunity to be as a role model in the promotion of cooperative evolution. Moreover, we also find the size of learning affects the choice of distant players, cooperators (defectors) having more paths to exploit the phalanx of opponents to survive when the value of preferential parameter is small. Besides, the extinction thresholds of cooperators and defectors for different values of noise are also investigated.
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McAndrew, Patrick, Rob Nadolski, and Alex Little. "Developing an approach for Learning Design Players." Journal of Interactive Media in Education 2005, no. 1 (August 17, 2005): 15. http://dx.doi.org/10.5334/2005-14.
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IKEDA, Hanako, Ryo Kato, Kazumi Kasahara, and Katsumi WATANABE. "Visuomotor sequential learning in action game players." Proceedings of the Annual Convention of the Japanese Psychological Association 75 (September 15, 2011): 3EV093. http://dx.doi.org/10.4992/pacjpa.75.0_3ev093.
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Protopapas, Mattheos K., Francesco Battaglia, and Elias B. Kosmatopoulos. "Coevolutionary Genetic Algorithms for Establishing Nash Equilibrium in Symmetric Cournot Games." Advances in Decision Sciences 2010 (May 12, 2010): 1–18. http://dx.doi.org/10.1155/2010/573107.
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We use coevolutionary genetic algorithms to model the players' learning process in several Cournot models and evaluate them in terms of their convergence to the Nash Equilibrium. The “social-learning” versions of the two coevolutionary algorithms we introduce establish Nash Equilibrium in those models, in contrast to the “individual learning” versions which, do not imply the convergence of the players' strategies to the Nash outcome. When players use “canonical coevolutionary genetic algorithms” as learning algorithms, the process of the game is an ergodic Markov Chain; we find that in the “social” cases states leading to NE play are highly frequent at the stationary distribution of the chain, in contrast to the “individual learning” case, when NE is not reached at all in our simulations; and finally we show that a large fraction of the games played are indeed at the Nash Equilibrium.
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WISEMAN, THOMAS. "SEQUENTIAL CHOICE AND NON-BAYESIAN OBSERVATIONAL LEARNING." International Game Theory Review 11, no. 03 (September 2009): 285–300. http://dx.doi.org/10.1142/s0219198909002327.
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Standard models of observational learning in settings of sequential choice have two key features. The first is that players make decisions by using Bayes' rule to update their beliefs about payoffs from a common prior. The second is that each agent's decision rule is common knowledge, so that subsequent players can draw inferences about unobserved private signals from observable actions. In this paper, I relax the first assumption while maintaining the second. In particular, I look at observational learning by players who choose between two actions using nonparametric methods for estimating payoffs. When players are identical and make inferences using the maximum score method, an informational cascade and herd must result. If players of different payoff types use kernel or nearest-neighbor methods, there are cases in which a cascade need not arise. If one does occur, it must be one in which all players, regardless of type, choose the same action. In some situations, these alternative learning rules perform better than Bayesian updating.
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Thompson, Meredith, Cigdem Uz-Bilgin, M. Shane Tutwiler, Melat Anteneh, Josephine Camille Meija, Annie Wang, Philip Tan, et al. "Immersion positively affects learning in virtual reality games compared to equally interactive 2d games." Information and Learning Sciences 122, no. 7/8 (July 19, 2021): 442–63. http://dx.doi.org/10.1108/ils-12-2020-0252.
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Purpose This study isolates the effect of immersion on players’ learning in a virtual reality (VR)-based game about cellular biology by comparing two versions of the game with the same level of interactivityand different levels of immersion. The authors identify immersion and additional interactivity as two key affordances of VR as a learning tool. A number of research studies compare VR with two-dimensional or minimally interactive media; this study focuses on the effect of immersion as a result of the head mounted display (HMD). Design/methodology/approach In the game, players diagnose a cell by exploring a virtual cell and search for clues that indicate one of five possible types of cystic fibrosis. Fifty-one adults completed all aspects of the study. Players took pre and post assessments and drew pictures of cells and translation before and after the game. Players were randomly assigned to play the game with the HMD (stereoscopic view) or without the headset (non-stereoscopic view). Players were interviewed about their drawings and experiences at the end of the session. Findings Players in both groups improved in their knowledge of the cell environment and the process of translation. Players who experienced the immersive stereoscopic view had a more positive learning effect in the content assessment, and stronger improvement in their mental models of the process of translation between pre- and post-drawings compared to players who played the two-dimensional game. Originality/value This study suggests that immersion alone has a positive effect on conceptual understanding, especially in helping learners understand spatial environments and processes. These findings set the stage for a new wave of research on learning in immersive environments; research that moves beyond determining whether immersive media correlate with more learning, toward a focus on the types of learning outcomes that are best supported by immersive media.
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Ozuak, Ali, and Atakan Çağlayan. "Differential Learning as an Important Factor in Training of Football Technical Skills." Journal of Education and Training Studies 7, no. 6 (May 10, 2019): 68. http://dx.doi.org/10.11114/jets.v7i6.4135.
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The aim of this study was to examine the effect of differential learning activities on young football players’ skills. Athletes who had played active football for at least 2 years in the youth teams participating in competitions in Amateur Leagues in Istanbul, Turkey took part in the study, as the Experimental Group (EG; n=26, age=12.03±0.44) and Control Group (CG, n=26, age=12.05±0.46). In the study, differential learning exercises integrated into their training programme for a period of 8 weeks, 3 days per week, were applied to the players in the EG immediately following warm-up, while the players in the CG continued with their traditional training programmes. The Illinois Test with Ball (ILL), Creative Speed Test (CST), Ball-Dribbling Test (DT), Ball-Juggling Test (JT) and Passing Test (PT) were carried out with all players participating in the study as a pretest prior to commencement of the programme and as a posttest following the implementation of the programme, and the gathered data were analyzed statistically. The findings obtained revealed that in the within-group pretest and posttest, players in the EG showed a statistically significant improvement in all parameters (p<0.05), while players in the CG showed a statistically significant improvement in ILL, CST, JT and PT (p<0.05). When the differences in development of the groups were compared, a statistically significant difference in the ILL, CST and DT parameters was determined in favor of the players in the EG (p<0.05). Consequently, although regularly-performed classic football training develops skills, it is seen that differential learning exercises integrated into training programmes are more effective for dribbling skills. It is considered that differential learning exercises, in which the non-dominant leg is frequently used, can make it easier for players to apply the necessary skills by allowing them to give more effective responses to the tricky positions encountered in football, and that these exercises can support the development of players’ performances.