Zeitschriftenartikel: „Player modeling“

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Konert, Johannes, Michael Gutjahr, Stefan Göbel und Ralf Steinmetz. „Modeling the Player“. International Journal of Game-Based Learning 4, Nr. 2 (April 2014): 36–50. http://dx.doi.org/10.4018/ijgbl.2014040103.

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For adaptation and personalization of game play sophisticated player models and learner models are used in game-based learning environments. Thus, the game flow can be optimized to increase efficiency and effectiveness of gaming and learning in parallel. In the field of gaming still the Bartle model is commonly used due to its simplicity and good mapping to game scenarios, for learning the Learning Style Inventory from Kolb or Index of Learning Styles by Felder and Silverman are well known. For personality traits the NEO-FFI (Big5) model is widely accepted. When designing games, it is always a challenge to assess one player's profile characteristics properly in all three models (player/learner/personality). To reduce the effort and amount of dimensions and questionnaires a player might have to fill out, we proved the hypothesis that both, Learning Style Inventory and Bartle Player Types could be predicted by knowing the personality traits based on NEO-FFI. Thus we investigated the statistical correlations among the models by collecting answers to the questionnaires of Bartle Test, Kolb LSI 3.1 and BFI-K (short version of NEO-FFI). A study was conducted in spring 2012 with six school classes of grade 9 (12-14 year old students) in two different secondary schools in Germany. 74 students participated in the study which was offered optionally after the use of a game-based learning tool for peer learning. We present the results statistics and correlations among the models as well as the interdependencies with the student's level of proficiency and their social connectedness. In conclusion, the evaluation (correlation and regression analyses) proved the independency of the models and the validity of the dimensions. Still, especially for all of the playing style preferences of Bartle's model significant correlations with some of the analyzed other questionnaire items could be found. As no predictions of learning style preferences is possible on the basis of this studies data, the final recommendation for the development of game-based learning application concludes that separate modeling for the adaptation game flow (playing) and learn flow (learning) is still necessary.

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Spronck, Pieter, und Freek Den Teuling. „Player Modeling in Civilization IV“. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 6, Nr. 1 (10.10.2010): 180–85. http://dx.doi.org/10.1609/aiide.v6i1.12409.

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This research aims at building a preference-based player model of Civilization IV players. Our model incorporates attributes which are defined for AI players. We use a sequential minimal optimization (SMO) classifier to build the player model based on a training set with observations of a large number of games between six AI players. The model was validated on a test set of games between the same six AI players. While it did not seem to generalize well to the preferences of different AI players, it did manage to accurately predict some of the preferences for a veteran human player. Further tests showed that AI players with the same play styles but different preference values were often confused by the model. We conclude that for a complex game such as Civilization IV a model that attempts to accurately predict specific preference values is hard to construct. A model that focusses on play styles might succeed better.

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Weber, Ben, Michael John, Michael Mateas und Arnav Jhala. „Modeling Player Retention in Madden NFL 11“. Proceedings of the AAAI Conference on Artificial Intelligence 25, Nr. 2 (11.08.2011): 1701–6. http://dx.doi.org/10.1609/aaai.v25i2.18864.

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Video games are increasingly producing huge datasets available for analysis resulting from players engaging in interactive environments. These datasets enable investigation of individual player behavior at a massive scale, which can lead to reduced production costs and improved player retention. We present an approach for modeling player retention in Madden NFL 11, a commercial football game. Our approach encodes gameplay patterns of specific players as feature vectors and models player retention as a regression problem. By building an accurate model of player retention, we are able to identify which gameplay elements are most influential in maintaining active players. The outcome of our tool is recommendations which will be used to influence the design of future titles in the Madden NFL series.

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Avontuur, Tetske, Pieter Spronck und Menno Van Zaanen. „Player Skill Modeling in Starcraft II“. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 9, Nr. 1 (30.06.2021): 2–8. http://dx.doi.org/10.1609/aiide.v9i1.12682.

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Starcraft II is a popular real-time strategy (RTS) game, in which players compete with each other online. Based on their performance, the players are ranked in one of seven leagues. In our research, we aim at constructing a player model that is capable of predicting the league in which a player competes, using observations of their in-game behavior. Based on cognitive research and our knowledge of the game, we extracted from 1297 game replays a number of features that describe skill. After a preliminary test, we selected the SMO classifier to construct a player model, which achieved a weighted accuracy of 47.3% (SD = 2.2). This constitutes a significant improvement over the weighted baseline of 25.5% (SD = 1.1). We tested from what moment in the game it is possible to predict a player’s skill, which we found is after about 2.5 minutes of gameplay, i.e., even before the players have confronted each other within the game. We conclude that our model can predict a player’s skill early in the game.

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Sawyer, Robert, Jonathan Rowe, Roger Azevedo und James Lester. „Modeling Player Engagement with Bayesian Hierarchical Models“. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 14, Nr. 1 (25.09.2018): 257–63. http://dx.doi.org/10.1609/aiide.v14i1.13048.

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Modeling player engagement is a key challenge in games. However, the gameplay signatures of engaged players can be highly context-sensitive, varying based on where the game is used or what population of players is using it. Traditionally, models of player engagement are investigated in a particular context, and it is unclear how effectively these models generalize to other settings and populations. In this work, we investigate a Bayesian hierarchical linear model for multi-task learning to devise a model of player engagement from a pair of datasets that were gathered in two complementary contexts: a Classroom Study with middle school students and a Laboratory Study with undergraduate students. Both groups of players used similar versions of Crystal Island, an educational interactive narrative game for science learning. Results indicate that the Bayesian hierarchical model outperforms both pooled and context-specific models in cross-validation measures of predicting player motivation from in-game behaviors, particularly for the smaller Classroom Study group. Further, we find that the posterior distributions of model parameters indicate that the coefficient for a measure of gameplay performance significantly differs between groups. Drawing upon their capacity to share information across groups, hierarchical Bayesian methods provide an effective approach for modeling player engagement with data from similar, but different, contexts.

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Poo Hernandez, Sergio, und Vadim Bulitko. „A Call for Emotion Modeling in Interactive Storytelling“. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 9, Nr. 4 (30.06.2021): 89–92. http://dx.doi.org/10.1609/aiide.v9i4.12633.

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Artificial Intelligence (AI) techniques are widely used in video games. Recently, AI planning methods have been applied to maintain plot consistency in the face of player’s agency over the narrative. Combined with an automatically populated player model, such AI experience managers can dynamically create a consistent narrative tailored to a specific player. These tools help game narrative designers achieve narrative goals while affording players a choice. On the other hand, they increase the number of feasible plot branches making it more difficult for the author to ensure that each branch carries the player along a desired emotion arc. In this paper we discuss the problem and call for an extension of experience managers with player emotion models. When successful, interactive narrative can be then automatically produced to satisfy authorial goals not only in terms of specific events but also in terms of emotions evoked in the player.

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Goslen, Alex, Dan Carpenter, Jonathan Rowe, Roger Azevedo und 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, Nr. 1 (11.10.2022): 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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Floyd, Calvin Michael, Matthew Hoffman und Ernest Fokoue. „Shot-by-shot stochastic modeling of individual tennis points“. Journal of Quantitative Analysis in Sports 16, Nr. 1 (26.03.2020): 57–71. http://dx.doi.org/10.1515/jqas-2018-0036.

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AbstractIndividual tennis points evolve over time and space, as each of the two opposing players are constantly reacting and positioning themselves in response to strikes of the ball. However, these reactions are diminished into simple tally statistics such as the amount of winners or unforced errors a player has. In this paper, a new way is proposed to evaluate how an individual tennis point is evolving, by measuring how many points a player can expect from each shot, given who struck the shot and where both players are located. This measurement, named “Expected Shot Value” (ESV), derives from stochastically modeling each shot of individual tennis points. The modeling will take place on multiple resolutions, differentiating between the continuous player movement and discrete events such as strikes occurring and duration of shots ending. Multi-resolution stochastic modeling allows for the incorporation of information-rich spatiotemporal player-tracking data, while allowing for computational tractability on large amounts of data. In addition to estimating ESV, this methodology will be able to identify the strengths and weaknesses of specific players, which will have the ability to guide a player’s in-match strategy.

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Min, Wookhee, Bradford Mott, Jonathan Rowe, Robert Taylor, Eric Wiebe, Kristy Boyer und James Lester. „Multimodal Goal Recognition in Open-World Digital Games“. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 13, Nr. 1 (25.06.2021): 80–86. http://dx.doi.org/10.1609/aiide.v13i1.12939.

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Recent years have seen a growing interest in player modeling to create player-adaptive digital games. As a core player-modeling task, goal recognition aims to recognize players’ latent, high-level intentions in a non-invasive fashion to deliver goal-driven, tailored game experiences. This paper reports on an investigation of multimodal data streams that provide rich evidence about players’ goals. Two data streams, game event traces and player gaze traces, are utilized to devise goal recognition models from a corpus collected from an open-world serious game for science education. Empirical evaluations of 140 players’ trace data suggest that multimodal LSTM-based goal recognition models outperform competitive baselines, including unimodal LSTMs as well as multimodal and unimodal CRFs, with respect to predictive accuracy and early prediction. The results demonstrate that player gaze traces have the potential to significantly enhance goal recognition models’ performance.

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Yu, Hong, und Tyler Trawick. „Personalized Procedural Content Generation to Minimize Frustration and Boredom Based on Ranking Algorithm“. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 7, Nr. 1 (09.10.2011): 208–13. http://dx.doi.org/10.1609/aiide.v7i1.12442.

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A growing research community is working towards procedurally generating content for computer games and simulation applications with various player modeling techniques. In this paper, we present a two-step procedural content generation framework to minimize players' frustration and/or boredom according to player feedback and gameplay features. In the first step, we dynamically categorize the player styles based on a simple questionnaire beforehand and the gameplay features. In the second step, two player models (frustration and boredom) are built for each player style category. A ranking algorithm is utilized for player modeling to address two problems inherent in player feedback: inconsistency and inaccuracy. Experiment results on a testbed game show that our framework can generate less boring/frustrating levels with very high probabilities.

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Gupta, Anisha, Dan Carpenter, Wookhee Min, Jonathan Rowe, Roger Azevedo und 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, Nr. 1 (11.10.2022): 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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Min, Wookhee, Alok Baikadi, Bradford Mott, Jonathan Rowe, Barry Liu, Eun Young Ha und James Lester. „A Generalized Multidimensional Evaluation Framework for Player Goal Recognition“. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 12, Nr. 1 (25.06.2021): 197–203. http://dx.doi.org/10.1609/aiide.v12i1.12880.

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Recent years have seen a growing interest in player modeling, which supports the creation of player-adaptive digital games. A central problem of player modeling is goal recognition, which aims to recognize players’ intentions from observable gameplay behaviors. Player goal recognition offers the promise of enabling games to dynamically adjust challenge levels, perform procedural content generation, and create believable NPC interactions. A growing body of work is investigating a wide range of machine learning-based goal recognition models. In this paper, we introduce GOALIE, a multidimensional framework for evaluating player goal recognition models. The framework integrates multiple metrics for player goal recognition models, including two novel metrics, n-early convergence rate and standardized convergence point. We demonstrate the application of the GOALIE framework with the evaluation of several player goal recognition models, including Markov logic network-based, deep feedforward neural network-based, and long short-term memory network-based goal recognizers on two different educational games. The results suggest that GOALIE effectively captures goal recognition behaviors that are key to next-generation player modeling.

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Sarratt, Trevor. „Leveraging Communication for Player Modeling and Cooperative Play“. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 10, Nr. 6 (29.06.2021): 14–17. http://dx.doi.org/10.1609/aiide.v10i6.12695.

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Collaboration between agents and players within games is a ripe area for exploration. As with adversarial AI, collaborative agents are challenged to accurately model players and adapt their behavior accordingly. The task of cooperation, however, allows for communication between teammates that can prove beneficial in coordinating joint actions and plans. Furthermore, we propose extending established multi-agent communication paradigms to include transfer of information pertinent to player models. By querying goal and preference information from a player, an agent can reduce uncertainty in coordination domains, allowing for more effective planning. We discuss the challenges as well as the planned development and evaluation of the system.

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Holmgård, Christoffer, Julian Togelius und Georgios Yannakakis. „Decision Making Styles as Deviation from Rational Action: A Super Mario Case Study“. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 9, Nr. 1 (30.06.2021): 142–48. http://dx.doi.org/10.1609/aiide.v9i1.12670.

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In this paper we describe a method of modeling play styles as deviations from approximations of game theoretically rational actions. These deviations are interpreted as containing information about player skill and player decision making style. We hypothesize that this information is useful for differentiating between players and for understanding why human player behavior is attributed intentionality which we argue is a prerequisite for believability. To investigate these hypotheses we describe an experiment comparing 400 games in the Mario AI Benchmark testbed, played by humans, with equivalent games played by an approximately game theoretically rationally playing AI agent. The player actions’ deviations from the rational agent’s actions are subjected to feature extraction, and the resulting features are used to cluster play sessions into expressions of different play styles. We discuss how these styles differ, and how believable agent behavior might be approached by using these styles as an outset for a planning agent. Finally, we discuss the implications of making assumptions about rational game play and the problematic aspects of inferring player intentions from behavior.

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Syufagi, Moh Aries, Mochamad Hariadi und 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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Revie, Matthew, Kevin J. Wilson, Rob Holdsworth und Stuart Yule. „On modeling player fitness in training for team sports with application to professional rugby“. International Journal of Sports Science & Coaching 12, Nr. 2 (27.02.2017): 183–93. http://dx.doi.org/10.1177/1747954117694736.

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It is increasingly important for professional sports teams to monitor player fitness in order to optimize performance. Models have been put forward linking fitness in training to performance in competition but rely on regular measurements of player fitness. As formal tests for measuring player fitness are typically time-consuming and inconvenient, measurements are taken infrequently. As such, it may be challenging to accurately predict performance in competition as player fitness is unknown. Alternatively, other data, such as how the players are feeling, may be measured more regularly. This data, however, may be biased as players may answer the questions differently and these differences may dominate the data. Linear mixed methods and support vector machines were used to estimate player fitness from available covariates at times when explicit measures of fitness were unavailable. Using data provided by a professional rugby club, a case study was used to illustrate the application and value of these models. Both models performed well with R2 values ranging from 60% to 85%, demonstrating that the models largely captured the biases introduced by individual players.

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Bunian, Sara, Alessandro Canossa, Randy Colvin und 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, Nr. 1 (25.06.2021): 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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Pedersen, C., J. Togelius und G. N. Yannakakis. „Modeling Player Experience for Content Creation“. IEEE Transactions on Computational Intelligence and AI in Games 2, Nr. 1 (März 2010): 54–67. http://dx.doi.org/10.1109/tciaig.2010.2043950.

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Krishnan, Abhijeet, Aaron Williams und Chris Martens. „Towards Action Model Learning for Player Modeling“. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 16, Nr. 1 (12.04.2021): 238–44. http://dx.doi.org/10.1609/aiide.v16i1.7436.

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Player modeling attempts to create a computational model which accurately approximates a player’s behavior in a game. Most player modeling techniques rely on domain knowledge and are not transferable across games. Additionally, player models do not currently yield any explanatory insight about a player’s cognitive processes, such as the creation and refinement of mental models. In this paper, we present our findings with using action model learning (AML), in which an action model is learned given data in the form of a play trace, to learn a player model in a domain-agnostic manner. We demonstrate the utility of this model by introducing a technique to quantitatively estimate how well a player understands the mechanics of a game. We evaluate an existing AML algorithm (FAMA) for player modeling and develop a novel algorithm called Blackout that is inspired by player cognition. We compare Blackout with FAMA using the puzzle game Sokoban and show that Blackout generates better player models.

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Gunes, M., Gokhan Solak, Ugur Akin, Omer Erden und Sanem Sariel. „A Generic Approach for Player Modeling Using Event-Trait Mapping and Feature Weighting“. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 12, Nr. 1 (25.06.2021): 169–75. http://dx.doi.org/10.1609/aiide.v12i1.12886.

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There are a wide variety of studies on player modeling. However, most of these studies target a specific game or genre. In some of these works, the number of in-game actions is used as a feature for modeling a player. However, using this feature leads to a complex model, and the model may miss some high-level relations among actions. In this paper, we propose a generic player modeling method that uses action-trait mapping relations which reveal correlations among actions. Mapping from the action-space to a much smaller trait-space improves interpretability of models. Additionally, to use the differences of impact of actions on player models, we apply feature weighting which uses the inverse of action frequencies. Players are clustered by Expectation Maximization. We demonstrate our method on a casual mobile game, Dusk Racer. We evaluate the feature weighting method using cluster validation with internal criteria. We conclude that using traits and feature weighting improves clustering quality and usability of the player model.

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Isaksen, Aaron, und Andy Nealen. „Comparing Player Skill, Game Variants, and Learning Rates Using Survival Analysis“. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 11, Nr. 5 (24.06.2021): 15–21. http://dx.doi.org/10.1609/aiide.v11i5.12846.

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Game designers can use computer-aided game design methods to quantitatively compare player skill levels, different game variants, and learning rates, for the purpose of modeling how players will likely experience a game. We use Monte-Carlo simulation, hazard functions, and survival analysis to show how difficulty will quantitatively change throughout a game level as we vary skill, game parameters, and learning rates. We give a mathematical overview of survival analysis, present empirical data analyses of our player models for each game variant, and provide theoretical probability distributions for each game. This analysis shows the quantitative reasons why balancing a game for a wide range of player skill can be difficult; our player modeling provides tools for tuning this game balance. We also analyze the score distribution of over 175 million play sessions of a popular online Flappy Bird variant to demonstrate how learning effects can impact scores, implying that learning is crucial aspect of player modeling.

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Butler, Eric. „Player Knowledge Modeling in Game Design Feedback and Automation“. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 9, Nr. 6 (30.06.2021): 2–5. http://dx.doi.org/10.1609/aiide.v9i6.12601.

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Models that capture the knowledge of players of digital games could be used to great effect in AI-assisted tools that automate or provide feedback for game design. There are several important tasks knowledge models should perform: predicting player performance on a particular task to adjust difficulty, knowing in which order to give particular concepts for maximum learning, or understanding how the pacing of a concept impacts player engagement. While all of these have been explored individual both in games and related fields like intelligent tutoring systems, there have been no models that capture all of these effects together in a way that allows their use in design tools. We propose to expand on previous work in game authoring tools to create tools in which the designer can leverage information about how players learn their game's concepts to create better designs. We will survey the existing player modeling work to find the best representation for this task, deploy these models in adaptive games to learn from data, and then apply these models to create novel game design tools.

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Henderson, Nathan, Wookhee Min, Jonathan Rowe und James Lester. „Multimodal Player Affect Modeling with Auxiliary Classifier Generative Adversarial Networks“. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 16, Nr. 1 (01.10.2020): 224–30. http://dx.doi.org/10.1609/aiide.v16i1.7434.

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Accurately detecting player affect is an important component of player modeling. Multimodal approaches to player modeling have shown significant promise because of their capacity to provide a multi-dimensional perspective on player behavior. However, obtaining sufficient data for training multimodal models of player affect presents significant challenges, including the prevalence of noisy, unbalanced, or missing data generated by multimodal sensor systems. To address this problem, we introduce a multimodal player affect modeling framework that improves player affect detection by using Auxiliary Classifier Generative Adversarial Networks (AC-GANs). We demonstrate the use of a Wasserstein distance-based approach for filtering synthesized data created in a data augmentation framework, and we investigate the effectiveness of the AC-GAN discriminator as an alternative approach for detecting player affect. Results show that AC-GAN based affective modeling outperforms baseline methods while enhancing player models through synthetic data generation and improved affect detection.

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Wu, Steven, und Luke Bornn. „Modeling Offensive Player Movement in Professional Basketball“. American Statistician 72, Nr. 1 (02.01.2018): 72–79. http://dx.doi.org/10.1080/00031305.2017.1395365.

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Gow, Jeremy, Robin Baumgarten, Paul Cairns, Simon Colton und Paul Miller. „Unsupervised Modeling of Player Style With LDA“. IEEE Transactions on Computational Intelligence and AI in Games 4, Nr. 3 (September 2012): 152–66. http://dx.doi.org/10.1109/tciaig.2012.2213600.

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Voronov, R. V., A. A. Rogov, A. V. Brilev und E. A. Petrov. „Modeling Media Player Switching between Bit Rates“. Journal of Physics: Conference Series 1352 (Oktober 2019): 012060. http://dx.doi.org/10.1088/1742-6596/1352/1/012060.

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Hooshyar, Danial, Moslem Yousefi und Heuiseok Lim. „Data-Driven Approaches to Game Player Modeling“. ACM Computing Surveys 50, Nr. 6 (12.01.2018): 1–19. http://dx.doi.org/10.1145/3145814.

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Quick, John M., und Robert K. Atkinson. „Modeling Gameplay Enjoyment, Goal Orientations, and Individual Characteristics“. International Journal of Game-Based Learning 4, Nr. 2 (April 2014): 51–77. http://dx.doi.org/10.4018/ijgbl.2014040104.

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The purpose of this study was to investigate the relationships between gameplay enjoyment, gaming goal orientations, and individual characteristics. A total of 301 participants were surveyed and the data were analyzed using structural equation modeling. This led to an expanded Gameplay Enjoyment Model (GEM) with 41 game design features that influence player enjoyment. Furthermore, a 3x2 Gaming Goal Orientations model was established with six dimensions that describe players' motivations for gaming. In addition, players' individual characteristics were used to predict gameplay enjoyment in the GEM-Individual Characteristics model. The six Gaming Goal Orientations dimensions were the strongest predictors, while the commonly used gender and hours played per week variables failed to predict enjoyment. The results of this study enable important work to be conducted surrounding gameplay experiences and individual characteristics. Ultimately, it is believed that the Gameplay Enjoyment Model, Gaming Goal Orientations, and the GEM-Individual Characteristics model will be useful tools for researchers and designers who seek to create effective gameplay experiences that meet the needs of players.

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Torkjazi, Mohammad, Nathan Huynh und Ali Asadabadi. „Modeling the Truck Appointment System as a Multi-Player Game“. Logistics 6, Nr. 3 (22.07.2022): 53. http://dx.doi.org/10.3390/logistics6030053.

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Background: Random truck arrivals at maritime container terminals are one of the primary reasons for gate congestion. Gate congestion negatively affects the terminal’s and drayage firms’ productivity and the surrounding communities in terms of air pollution and noise. To alleviate gate congestion, more and more terminals in the USA are utilizing a truck appointment system (TAS). Methods: This paper proposes a novel approach to modeling the truck appointment system problem. Unlike previous studies which largely treated this problem as a single-player game, this study explicitly models the interplay between the terminal and drayage firms with regard to appointments. A multi-player bi-level programming model is proposed, where the terminal functions as the leader at the upper-level and the drayage firms function as followers at the lower-level. The objective of the leader (the terminal) is to minimize the gate waiting cost of trucks by spreading out the truck arrivals, and the objective of the followers (drayage firms) is to minimize their own drayage cost. To make the model tractable, the bi-level model is transformed to a single-level problem by replacing the lower-level problem with its equivalent Karush–Kuhn–Tucker (KKT) conditions and the model is solved by finding the Stackelberg equilibrium in one-shot simultaneous-moves among players. For comparison purposes, a single-player version of the TAS model is also developed. Results: Experimental results indicate that the proposed multi-player model yields a lower gate-waiting cost compared to the single-player model, and that it yields higher cost savings for the drayage firms as the number of appointments per truck increases. Moreover, the solution of the multi-player model is not dependent on the objective function coefficients, unlike the single player model. Conclusions: This study demonstrates that a TAS is more effective if it considers how the assigned appointment slot affects a truck’s drayage cost. It is recommended that terminal operators and port authorities initiate conversations with their TAS providers about incorporating this element into their TAS.

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Kermanidis, Katia Lida. „Identifying Latent Semantics in Action Games for Player Modeling“. International Journal of Gaming and Computer-Mediated Simulations 11, Nr. 2 (April 2019): 1–21. http://dx.doi.org/10.4018/ijgcms.2019040101.

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Machine learning approaches to player modeling traditionally employ a high-level game-knowledge-based feature for representing game sessions, and often player behavioral features as well. The present work makes use of generic low-level features and latent semantic analysis for unsupervised player modeling, but mostly for revealing underlying hidden information regarding game semantics that is not easily detectable beforehand.

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Valls-Vargas, Josep, Santiago Ontañón und Jichen Zhu. „Exploring Player Trace Segmentation for Dynamic Play Style Prediction“. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 11, Nr. 1 (24.06.2021): 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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Holmgård, Christoffer, Antonios Liapis, Julian Togelius und Georgios Yannakakis. „Monte-Carlo Tree Search for Persona Based Player Modeling“. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 11, Nr. 5 (24.06.2021): 8–14. http://dx.doi.org/10.1609/aiide.v11i5.12849.

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Is it possible to conduct player modeling without any players? In this paper we use Monte-Carlo Tree Search-controlled procedural personas to simulate a range of decision making styles in the puzzle game MiniDungeons 2. The purpose is to provide a method for synthetic play testing of game levels with synthetic players based on designer intuition and experience. Five personas are constructed, representing five different decision making styles archetypal for the game. The personas vary solely in the weights of decision-making utilities that describe their valuation of a set affordances in MiniDungeons 2. By configuring these weights using designer expert knowledge, and passing the configurations directly to the MCTS algorithm, we make the personas exhibit a number of distinct decision making and play styles.

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Wang, Zhikun, Abdeslam Boularias, Katharina Mülling und Jan Peters. „Modeling Opponent Actions for Table-Tennis Playing Robot“. Proceedings of the AAAI Conference on Artificial Intelligence 25, Nr. 1 (04.08.2011): 1828–29. http://dx.doi.org/10.1609/aaai.v25i1.8051.

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Opponent modeling is a critical mechanism in repeated games. It allows a player to adapt its strategy in order to better respond to the presumed preferences of its opponents. We introduce a modeling technique that adaptively balances safety and exploitability. The opponent's strategy is modeled with a set of possible strategies that contains the actual one with high probability. The algorithm is safe as the expected payoff is above the minimax payoff with high probability, and can exploit the opponent's preferences when sufficient observations are obtained. We apply the algorithm to a robot table-tennis setting where the robot player learns to prepare to return a served ball. By modeling the human players, the robot chooses a forehand, backhand or middle preparation pose before they serve. The learned strategies can exploit the opponent's preferences, leading to a higher rate of successful returns.

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NASH, JOHN F. „THE AGENCIES METHOD FOR MODELING COALITIONS AND COOPERATION IN GAMES“. International Game Theory Review 10, Nr. 04 (Dezember 2008): 539–64. http://dx.doi.org/10.1142/s0219198908002084.

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The idea leading to this study originated some time ago when I talked at a gathering of high school graduates at a summer science camp. I spoke about the theme of "the evolution of cooperation" (in Nature) and about how that topic was amenable to studies involving Game Theory (which, more frequently, has been used in research in economics). After that event I was stimulated to think of the possibility of modeling cooperation in games through actions of acceptance in which one player could simply accept the "agency" of another player or of an existing coalition of players. The action of acceptance would have the form of being entirely cooperative, as if "altruistic", and not at all competitive, but there was also the idea that the game would be studied under circumstances of repetition and that every player would have the possibility of reacting in a non-cooperative fashion to any undesirable pattern of behavior of any another player. Thus the game studied would be analogous to the repeated games of "Prisoner's Dilemma" variety that have been studied in theoretical biology. These studies of "PD" (or "Prisoner's Dilemma") games have revealed the paradoxical possibility of the natural evolution of cooperative behavior when the interacting organisms or species are presumed only to be endowed with self-interested motivations, thus motivations of a non-cooperative type.

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Iskandar Jurgensen, Stefan Ch, und Singmin Johanes Lo. „THE IMPACT OF LEADERSHIP STYLE AND ORGANIZATIONAL CULTURE TOWARDS PLAYER PERFORMANCE THROUGH PLAYER'S WORK SATISFACTION AT BINTANG PRATAMA BASKETBALL CLUB“. Dinasti International Journal of Digital Business Management 2, Nr. 1 (10.12.2020): 146–54. http://dx.doi.org/10.31933/dijdbm.v2i1.642.

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This research purpose way to explored those influence from leadership style towards performance and players' work satisfaction at Bintang Pratama Basketball Club. These research method used quantitative approach. Population and sample were 108 respondents whose members of the club. Data analysis method in this research used Structural Equation Modeling (SEM) with assist of SmartPLS version 3.0 software. The research results indicated that leadership style has a positive and significant impact towards player performance. The leadership style has proven to have a positive and significant influence over the player's work satisfaction. Organizational culture has proven not to have a positive and significant influence towards player performance. Organizational culture has proven to have a positive and significant control across players' work satisfaction. Player's work satisfaction has proven to have a positive and significant control over player performance. Work satisfaction has been shown to mediate the influence of leadership style towards player performance. Work satisfaction has proven to mediate those influence from organizational culture towards player performance.

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Wiggins, Joseph, Mayank Kulkarni, Wookhee Min, Bradford Mott, Kristy Boyer, Eric Wiebe und James Lester. „Affect-Based Early Prediction of Player Mental Demand and Engagement for Educational Games“. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 14, Nr. 1 (25.09.2018): 243–49. http://dx.doi.org/10.1609/aiide.v14i1.13047.

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Player affect is a central consideration in the design of game-based learning environments. Affective indicators such as facial expressions exhibited during gameplay may support building more robust player models and adaptation modules. In game-based learning, predicting player mental demand and engagement from player affect is a particularly promising approach to helping create more effective gameplay. This paper reports on a predictive player-modeling approach that observes player affect during early interactions with a game-based learning environment and predicts selfreports of mental demand and engagement at the conclusion of gameplay sessions. The findings show that automatically detected facial expressions such as those associated with joy, disgust, sadness, and surprise are significant predictors of players’ self-reported engagement and mental demand at the end of gameplay interactions. The results suggest that it is possible to create affect-based predictive player models that can enable proactively tailored gameplay by anticipating player mental demand and engagement.

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Ramirez, Alejandro, und Vadim Bulitko. „Automated Planning and Player Modeling for Interactive Storytelling“. IEEE Transactions on Computational Intelligence and AI in Games 7, Nr. 4 (Dezember 2015): 375–86. http://dx.doi.org/10.1109/tciaig.2014.2346690.

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Park, Hyunsoo, und Kyung-Joong Kim. „Active Player Modeling in the Iterated Prisoner’s Dilemma“. Computational Intelligence and Neuroscience 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/7420984.

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The iterated prisoner’s dilemma (IPD) is well known within the domain of game theory. Although it is relatively simple, it can also elucidate important problems related to cooperation and trust. Generally, players can predict their opponents’ actions when they are able to build a precise model of their behavior based on their game playing experience. However, it is difficult to make such predictions based on a limited number of games. The creation of a precise model requires the use of not only an appropriate learning algorithm and framework but also a good dataset. Active learning approaches have recently been introduced to machine learning communities. The approach can usually produce informative datasets with relatively little effort. Therefore, we have proposed an active modeling technique to predict the behavior of IPD players. The proposed method can model the opponent player’s behavior while taking advantage of interactive game environments. This experiment used twelve representative types of players as opponents, and an observer used an active modeling algorithm to model these opponents. This observer actively collected data and modeled the opponent’s behavior online. Most of our data showed that the observer was able to build, through direct actions, a more accurate model of an opponent’s behavior than when the data were collected through random actions.

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Wang, Zhikun, Abdeslam Boularias, Katharina Mülling und Jan Peters. „Balancing Safety and Exploitability in Opponent Modeling“. Proceedings of the AAAI Conference on Artificial Intelligence 25, Nr. 1 (04.08.2011): 1515–20. http://dx.doi.org/10.1609/aaai.v25i1.7981.

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Opponent modeling is a critical mechanism in repeated games. It allows a player to adapt its strategy in order to better respond to the presumed preferences of his opponents. We introduce a new modeling technique that adaptively balances exploitability and risk reduction. An opponent’s strategy is modeled with a set of possible strategies that contain the actual strategy with a high probability. The algorithm is safe as the expected payoff is above the minimax payoff with a high probability, and can exploit the opponents’ preferences when sufficient observations have been obtained. We apply them to normal-form games and stochastic games with a finite number of stages. The performance of the proposed approach is first demonstrated on repeated rock-paper-scissors games. Subsequently, the approach is evaluated in a human-robot table-tennis setting where the robot player learns to prepare to return a served ball. By modeling the human players, the robot chooses a forehand, backhand or middle preparation pose before they serve. The learned strategies can exploit the opponent’s preferences, leading to a higher rate of successful returns.

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Supola, Bence, Thomas Hoch und Arnold Baca. „Modeling the formation of defensive gaps in basketball: Cutting on a teammate’s drive“. PLOS ONE 18, Nr. 2 (07.02.2023): e0281467. http://dx.doi.org/10.1371/journal.pone.0281467.

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Basketball is a game of simultaneous actions, and inter-player coordination is key for offensive success. One of the most challenging aspects in this regard is basket cutting on a teammate’s drive. The ability to make these cuts is considered to be an artistic skill, mastered by only a handful of players. This skill is also hard to assess, as there is no method to measure the players’ capability with respect to this quality–especially not automatically. Using SportVU data from the NBA, we created a mathematical model that identifies the openings in the defense which allow to perform a cut. Our model succeeds to generalize, as it detects these openings on average 139ms earlier than the actual cuts start and has an overall (balanced) accuracy of 0.818 on the test set. Having a tree-based gradient boosting classifier, we received a clear hierarchy of feature importance and were able to inspect the interactions between these attributes during action. This way, the model gives insights about the kind of defensive movements needed for a player to allow enough space to cut while in practical usage the analysis of the output can also help the coaching staff in designing play options and assessing player abilities. By paying more attention to the possible off ball movements during drives, offensive plays can become more versatile–benefiting the participants and the spectators alike.

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Treanor, Mike, Josh McCoy und Anne Sullivan. „Social Play in Non-Player Character Dialog“. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 11, Nr. 4 (24.06.2021): 99–101. http://dx.doi.org/10.1609/aiide.v11i4.12838.

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Non-player characters in games generally lack believability and deep interactivity. The AI system Comme il Faut begins to tackle this by modeling social state and behaviors for game characters. The player initiates social exchanges and the dialog and outcome are generated and displayed in their entirety. In this paper we present a model called social prac-tices to extend Comme il Faut. Social practices increase the playability of social play by modeling social interactions at a more granular level and adding interactivity at each stage. This model also moves away from dialog trees to a more modular form of authoring to support the additional com-plexity.

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Mustač, Kuzma, Krešimir Bačić, Lea Skorin-Kapov und Mirko Sužnjević. „Predicting Player Churn of a Free-to-Play Mobile Video Game Using Supervised Machine Learning“. Applied Sciences 12, Nr. 6 (09.03.2022): 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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Yigit, Ahmet Talha, Baris Samak und Tolga Kaya. „An XGBoost-lasso ensemble modeling approach to football player value assessment“. Journal of Intelligent & Fuzzy Systems 39, Nr. 5 (19.11.2020): 6303–14. http://dx.doi.org/10.3233/jifs-189098.

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Sports analytics is a field that is growing in popularity and application throughout the world. One of the open problems in this field is the valuation of football players. The aim of this study is to establish a football player value assessment model using machine learning techniques to support the transfer decisions of football clubs. The proposed model is mainly based on the intrinsic features of the individual players which are provided in Football Manager simulation game. To do this, based on the individual statistics of 5316 players who are active in 11 different major leagues from Europe and South America, different value assessment models are conducted using advanced supervised learning techniques which include ridge and lasso regressions, random forests and extreme gradient boosting. All the models have been built in R programming language. The performances of the models are compared based on their mean squared errors and their fit to the real world examples. An ensemble model with inflation is proposed as the output.

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Chen, Hang. „A Data Mining-Based Model for Evaluating Tennis Players’ Training Movements“. Discrete Dynamics in Nature and Society 2022 (21.02.2022): 1–11. http://dx.doi.org/10.1155/2022/8950732.

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This paper uses data mining technology to mathematically model the training movements of tennis players, establish a three-dimensional data information database of athletes utilizing depth imaging, analyze the data with data mining algorithms, and derive the results after comparative evaluation and analysis with a database of movement characteristics of tennis dribblers. This paper uses video observation and mathematical modeling to construct a tennis player training action evaluation model, which provides a reference basis for tennis players to improve and enhance their tactical level; it can also provide a reference for the development of sports training special theory of tennis projects and enrich the tactical diagnosis method of tennis matches. To improve the accuracy of 3D human pose estimation, this paper adopts a 3D skeleton point extraction method based on RGBD images; for the action alignment problem, this paper uses a dynamic time warping (DTW) algorithm; for the similarity measure, this paper gives a Pearson correlation coefficient method based on the joint point features of human parts. This paper aims to conduct a systematic theoretical analysis of tennis players’ training movements based on theories and methods such as system science theory and social network analysis. On this basis, the characteristics of tennis training technology development are analyzed from a combination of qualitative and quantitative perspectives, while the development of tennis player training is explored based on tracking observations of tennis player movement training, and finally, the attack and service characteristics of tennis training are analyzed to better provide some reference for the sustainable development of tennis.

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Saadat, Samaneh, und Gita Sukthankar. „Contrast Motif Discovery in Minecraft“. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 16, Nr. 1 (01.10.2020): 266–72. http://dx.doi.org/10.1609/aiide.v16i1.7440.

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Understanding event sequences is an important aspect of game analytics, since it is relevant to many player modeling questions. This paper introduces a method for analyzing event sequences by detecting contrasting motifs; the aim is to discover subsequences that are significantly more similar to one set of sequences vs. other sets. Compared to existing methods, our technique is scalable and capable of handling long event sequences. We applied our proposed sequence mining approach to analyze player behavior in Minecraft, a multiplayer online game that supports many forms of player collaboration. As a sandbox game, it provides players with a large amount of flexibility in deciding how to complete tasks; this lack of goal-orientation makes the problem of analyzing Minecraft event sequences more challenging than event sequences from more structured games. Using our approach, we were able to discover contrast motifs for many player actions, despite variability in how different players accomplished the same tasks. Furthermore, we explored how the level of player collaboration affects the contrast motifs. Although this paper focuses on applications within Minecraft, our tool, which we have made publicly available along with our dataset, can be used on any set of game event sequences.

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Ван, Чен, Chen Wang, Владимир Викторович Мазалов, Vladimir Mazalov, Хунвей Гао und Hongwei Gao. „Controlling opinion dynamics and consensus and in a social network“. Mathematical Game Theory and Applications 12, Nr. 4 (23.12.2020): 24–39. http://dx.doi.org/10.17076/mgta_2020_4_24.

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A game-theoretic model of the influence of players on the dynamics of opinions and the achieved consensus in the social network is considered. The goal of a player is to maintain the opinion of all participants in the vicinity of a predetermined value. If there are several players, then these target values are they can be different. The dynamic game belongs to the class of linear-quadratic games in discrete time. Optimal control and equilibrium are found using the Bellman equation. The solution is achieved in an analytical form. It is shown that in the model with one player, a controlled consensus is achieved in the social network. The two-player model shows that although there is no consensus in the social network, the equilibrium is completely determined by the mean value of the opinion of all participants, which converges to a certain value. The results of numerical modeling for a social network with one and two players are presented.

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Kosa, Mehmet, Ahmet Uysal und P. Erhan Eren. „Acceptance of Virtual Reality Games“. International Journal of Gaming and Computer-Mediated Simulations 12, Nr. 1 (Januar 2020): 43–70. http://dx.doi.org/10.4018/ijgcms.2020010103.

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As virtual reality (VR) games are getting more widespread, the need to understand the interaction between players and the VR games is gaining prominence. The present study examines player endorsement of virtual reality games from an amalgamation of technology acceptance, self-determination, and flow theory perspectives. A survey was carried out with participants (N = 396) who had played a VR game at least once and at most five times. Structural equation modeling analyses showed that perceived ease of use was the primary predictor for satisfaction of self-determination constructs (autonomy and competence) and flow constructs (immersion and concentration), which in turn predicted player enjoyment. Accordingly, the results suggest the importance of including self-determination constructs in addition to the flow perspective within the context of technology acceptance model, for explaining the acceptance of VR gaming. Findings also showed that enjoyment resulted in positive attitudes towards VR gaming, and these attitudes predicted intention to play VR games.

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Zhao, Sha, Yizhi Xu, Zhiling Luo, Jianrong Tao, Shijian Li, Changjie Fan und Gang Pan. „Player Behavior Modeling for Enhancing Role-Playing Game Engagement“. IEEE Transactions on Computational Social Systems 8, Nr. 2 (April 2021): 464–74. http://dx.doi.org/10.1109/tcss.2021.3052261.

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Sharma, Manu, Santiago Ontañón, Manish Mehta und Ashwin Ram. „DRAMA MANAGEMENT AND PLAYER MODELING FOR INTERACTIVE FICTION GAMES“. Computational Intelligence 26, Nr. 2 (Mai 2010): 183–211. http://dx.doi.org/10.1111/j.1467-8640.2010.00355.x.

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Vahlo, Jukka, Jouni Smed und Aki Koponen. „Validating gameplay activity inventory (GAIN) for modeling player profiles“. User Modeling and User-Adapted Interaction 28, Nr. 4-5 (13.11.2018): 425–53. http://dx.doi.org/10.1007/s11257-018-9212-y.

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