Relevant bibliographies by topics / Player modeling

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Player modeling'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 March 2023

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Journal articles on the topic "Player modeling"

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Konert, Johannes, Michael Gutjahr, Stefan Göbel, and Ralf Steinmetz. "Modeling the Player." International Journal of Game-Based Learning 4, no. 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, and Freek Den Teuling. "Player Modeling in Civilization IV." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 6, no. 1 (October 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, and Arnav Jhala. "Modeling Player Retention in Madden NFL 11." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 2 (August 11, 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, and Menno Van Zaanen. "Player Skill Modeling in Starcraft II." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 9, no. 1 (June 30, 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, and James Lester. "Modeling Player Engagement with Bayesian Hierarchical Models." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 14, no. 1 (September 25, 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, and Vadim Bulitko. "A Call for Emotion Modeling in Interactive Storytelling." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 9, no. 4 (June 30, 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, 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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Floyd, Calvin Michael, Matthew Hoffman, and Ernest Fokoue. "Shot-by-shot stochastic modeling of individual tennis points." Journal of Quantitative Analysis in Sports 16, no. 1 (March 26, 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, and James Lester. "Multimodal Goal Recognition in Open-World Digital Games." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 13, no. 1 (June 25, 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, and 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, no. 1 (October 9, 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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Dissertations / Theses on the topic "Player modeling"

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Anghileri, Davide. "Using Player Modeling to Improve Automatic Playtesting." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-232059.

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In this thesis we present two approaches to improve automatic playtesting using player modeling. By modeling various cohorts of players we are able to train Convolutional Neural Network based agents that simulate human gameplay using different strategies directly learnt from real player data. The goal is to use the developed agents to predict useful metrics of newly created game content. We validated our approaches using the game Candy Crush Saga, a non-deterministic match-three puzzle game with a huge search space and more than three thousand levels available. To the best of our knowledge this is the first time that player modeling is applied in a match-three puzzle game. Nevertheless, the presented approaches are general and can be extended to other games as well. The proposed methods are compared to a baseline approach that simulates gameplay using a single strategy learnt from random gameplay data. Results show that by simulating different strategies, our approaches can more accurately predict the level difficulty, measured as the players’ success rate, on new levels. Both the approaches improved the mean absolute error by 13% and the mean squared error by approximately 23% when predicting with linear regression models. Furthermore, the proposed approaches can provide useful insights to better understand the players and the game.
I denna uppsats presenterar vi två tillvägagångssätt för att förbättra automatisk speltestning genom modellering av spelare. Genom att modellera olika grupper av spelare kunde vi träna Convolutional Neural Network-baserade agenter för att simulera mänskligt spelande med hjälp av olika strategier som är lärda direkt från mänsklig spelardata. Målet är att använda de utvecklade agenterna för att förutsäga användbar metrik av nyskapat spelinnehåll. Vi validerade vårt tillvägagångssätt genom Candy Crush Saga, ett icke-deterministiskt 3-matchnings pusselspel med mer än tre tusen nivåer. Detta är första gången som spelarmodellering appliceras på ett 3-matchnings pusselspel. De presenterade tillvägagångssätten är mer generella och kan utökas till andra spel. De föreslagna tillvägagångssätten är jämförda med ett tillvägagångssätt som simulerar spelande genom en strategi som är lärd direkt från slumpmässig mänsklig spelardata. Resultatet visar att vårt tillvägagångssätt, genom simulering av olika strategier är, mer exakt för att förutsäga spelarens svårighet, mätt genom spelarens framgång, på nya nivåer. Båda tillvägagångssätten förbättrade mean absolute error med 13% och mean squared error med ungefär 23%. Dessutom kan de föreslagna tillvägagångssätten ge en användbar insikt för att bättre förstå spelarna och spelet.
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Malkan, Nelson Anna. "Messages in games and player backgroundA player study about modeling and conveying emotional states through game rules and mechanics : A player study about modeling and conveying emotional states through game rules and mechanics." Thesis, Uppsala universitet, Institutionen för speldesign, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-414365.

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Games can be used to convey meaning and communicate messages. While there is ample research on games' expressive capacities, how players' backgrounds impact game interpretation has thus far been under-explored.  This study explores this gap by way of testing an expressive game and discerns if there is a relationship between how people experience a game and their personal background and current state of mind. To engage this question, we conducted a player study. We developed the abstract, metaphorical game “Lorn” intended for this purpose. The game together with an online survey, intended to assess players' background and state of mind, was distributed to potential participants. After having played the game, the participants shared their interpretation of the message in the game and their experiences and feelings they experienced while playing. 15 people participated in the player study. The result indicates there are differences in how people interpret a message depending on their personal background and their current state of mind.
Spel kan användas för att förmedla både budskap och mening. Trots att det finns omfattande forskning på hur man kan uttrycka sig med spel, så är forskning kring hur spelares bakgrund påverkar deras tolkning bristfällig. Den här studien utforskar detta genom att testa ett “expressivt spel” och urskilja om det finns någon koppling mellan hur människor upplever ett spel och deras personliga bakgrund och sinnesstämning. Vi utförde en spelarstudie för att undersöka den här frågan. För detta ändamål utvecklade vi det abstrakta, metaforiska spelet “Lorn”. Tillsammans med en online enkät, som ämnade att ta reda på spelarnas bakgrund och sinnesstämning, distribuerade vi spelet till potentiella deltagare. Efter att ha spelat spelet delgav deltagarna sina tolkningar av betydelsen, sina upplevelser av Lorn, och vilka känslor de kände när de spelade spelet. 15 personer deltog i studien. Våra resultat indikerar att det finns skillnader i hur människor tolkar budskap beroende på deras personliga bakgrund och sinnesstämning.
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Lim, Chong-U. "Modeling player self-representation in multiplayer online games using social network data." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/82409.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 101-105).
Game players express values related to self-expression through various means such as avatar customization, gameplay style, and interactions with other players. Multiplayer online games are now often integrated with social networks that provide social contexts in which player-to-player interactions take place, such as conversation and trading of virtual items. Building upon a theoretical framework based in machine learning and cognitive science, I present results from a novel approach to modeling and analyzing player values in terms of both preferences in avatar customization and patterns in social network use. To facilitate this work, I developed the Steam-Player- Preference Analyzer (Steam-PPA) system, which performs advanced data collection on publicly available social networking profile information. The primary contribution of this thesis is the AIR Toolkit Status Performance Classifier (AIR-SPC), which uses machine learning techniques including k-means clustering, natural language processing (NLP), and support vector machines (SVM) to perform inference on the data. As an initial case study, I use Steam-PPA to collect gameplay and avatar customization information from players in the popular, and commercially successful, multi-player first-person-shooter game Team Fortress 2 (TF2). Next, I use AIR-SPC to analyze the information from profiles on the social network Steam. The upshot is that I use social networking information to predict the likelihood of players customizing their profile in several ways associated with the monetary values of their avatars. In this manner I have developed a computational model of aspects of players' digital social identity capable of predicting specific values in terms of preferences exhibited within a virtual game-world.
by Chong-U Lim.
S.M.
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Loria, Enrica. "Alone with Company: Studying Individual and Social Players' In-game Behaviors in Adaptive Gamification." Doctoral thesis, Università degli studi di Trento, 2004. http://hdl.handle.net/11572/299790.

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Humans procrastinate and avoid performing activities that they deem dull, repetitive, and out of their comfort zone. Gamification was conceived to reverse the situation by turning those activities into fun and entertaining actions exploiting game-like elements. In practice, however, many challenges arise. Gameful environments cannot satisfy every player's preference and motivational need with a one-fits-all strategy. However, meeting players' motivational affordances can provide intrinsic rewards rather than extrinsic (e.g., points and badges). Producing intrinsic rewards is desirable as they are more likely to foster long-term retention than the extrinsic counterpart. Therefore, gamified systems should be designed to learn and understand players' preferences and motivational drivers to generate specific adaptation strategies for each player. Those adaptation strategies govern the procedural generation of personalized game elements - examples are task difficulty, social-play versus solo-play, or aesthetic tools. However, an appropriate personalization requires intelligent and effective player profiling mechanisms. Player profiles can be retrieved through the analysis of telemetry data, and thus in-game behaviors. In this project, we studied players' individual and social behaviors to understand their personalities and identities within the game. Specifically, we analyzed data from an open-world, persuasive, gamified system: Play&Go. Play&Go implements game-like mechanics to instill more ecological transportation habits among its users. The gamified app offers various ways for players to interact with the game and among one another. Despite Play&Go being one of the few examples of gamification implementing more diverse game mechanics than solely points and leaderboards, it still does not reach the complexity of AAA entertainment games. Thus, it limits the applicability of an in-depth analysis of players' behaviors, constrained by the type of available features. Yet, we argue that gameful systems still provide enough information to allow content adaptation. In this work, we study players' in-game activity from different perspectives to explore gamification's potential. Towards this, we analyzed telemetry data to (1) learn from players' activity, (2) extract their profiles, and (3) understand social dynamics in force within the game. Our results show how players' experience in gamified systems is closer to games than expected, especially in social environments. Hence, telemetry data is a precious source of knowledge also in gamification and can help retain information on players' churn, preferences, and social influence. Finally, we propose a modular theoretical framework for adaptive gamification to generate personalized content designed to learn players' preferences iteratively.
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Loria, Enrica. "Alone with Company: Studying Individual and Social Players' In-game Behaviors in Adaptive Gamification." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/299790.

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Humans procrastinate and avoid performing activities that they deem dull, repetitive, and out of their comfort zone. Gamification was conceived to reverse the situation by turning those activities into fun and entertaining actions exploiting game-like elements. In practice, however, many challenges arise. Gameful environments cannot satisfy every player's preference and motivational need with a one-fits-all strategy. However, meeting players' motivational affordances can provide intrinsic rewards rather than extrinsic (e.g., points and badges). Producing intrinsic rewards is desirable as they are more likely to foster long-term retention than the extrinsic counterpart. Therefore, gamified systems should be designed to learn and understand players' preferences and motivational drivers to generate specific adaptation strategies for each player. Those adaptation strategies govern the procedural generation of personalized game elements - examples are task difficulty, social-play versus solo-play, or aesthetic tools. However, an appropriate personalization requires intelligent and effective player profiling mechanisms. Player profiles can be retrieved through the analysis of telemetry data, and thus in-game behaviors. In this project, we studied players' individual and social behaviors to understand their personalities and identities within the game. Specifically, we analyzed data from an open-world, persuasive, gamified system: Play&Go. Play&Go implements game-like mechanics to instill more ecological transportation habits among its users. The gamified app offers various ways for players to interact with the game and among one another. Despite Play&Go being one of the few examples of gamification implementing more diverse game mechanics than solely points and leaderboards, it still does not reach the complexity of AAA entertainment games. Thus, it limits the applicability of an in-depth analysis of players' behaviors, constrained by the type of available features. Yet, we argue that gameful systems still provide enough information to allow content adaptation. In this work, we study players' in-game activity from different perspectives to explore gamification's potential. Towards this, we analyzed telemetry data to (1) learn from players' activity, (2) extract their profiles, and (3) understand social dynamics in force within the game. Our results show how players' experience in gamified systems is closer to games than expected, especially in social environments. Hence, telemetry data is a precious source of knowledge also in gamification and can help retain information on players' churn, preferences, and social influence. Finally, we propose a modular theoretical framework for adaptive gamification to generate personalized content designed to learn players' preferences iteratively.
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Loria, Enrica. "Alone with Company: Studying Individual and Social Players' In-game Behaviors in Adaptive Gamification." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/299790.

Full text
Abstract:
Humans procrastinate and avoid performing activities that they deem dull, repetitive, and out of their comfort zone. Gamification was conceived to reverse the situation by turning those activities into fun and entertaining actions exploiting game-like elements. In practice, however, many challenges arise. Gameful environments cannot satisfy every player's preference and motivational need with a one-fits-all strategy. However, meeting players' motivational affordances can provide intrinsic rewards rather than extrinsic (e.g., points and badges). Producing intrinsic rewards is desirable as they are more likely to foster long-term retention than the extrinsic counterpart. Therefore, gamified systems should be designed to learn and understand players' preferences and motivational drivers to generate specific adaptation strategies for each player. Those adaptation strategies govern the procedural generation of personalized game elements - examples are task difficulty, social-play versus solo-play, or aesthetic tools. However, an appropriate personalization requires intelligent and effective player profiling mechanisms. Player profiles can be retrieved through the analysis of telemetry data, and thus in-game behaviors. In this project, we studied players' individual and social behaviors to understand their personalities and identities within the game. Specifically, we analyzed data from an open-world, persuasive, gamified system: Play&Go. Play&Go implements game-like mechanics to instill more ecological transportation habits among its users. The gamified app offers various ways for players to interact with the game and among one another. Despite Play&Go being one of the few examples of gamification implementing more diverse game mechanics than solely points and leaderboards, it still does not reach the complexity of AAA entertainment games. Thus, it limits the applicability of an in-depth analysis of players' behaviors, constrained by the type of available features. Yet, we argue that gameful systems still provide enough information to allow content adaptation. In this work, we study players' in-game activity from different perspectives to explore gamification's potential. Towards this, we analyzed telemetry data to (1) learn from players' activity, (2) extract their profiles, and (3) understand social dynamics in force within the game. Our results show how players' experience in gamified systems is closer to games than expected, especially in social environments. Hence, telemetry data is a precious source of knowledge also in gamification and can help retain information on players' churn, preferences, and social influence. Finally, we propose a modular theoretical framework for adaptive gamification to generate personalized content designed to learn players' preferences iteratively.
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Mathema, Najma. "Predicting Plans and Actions in Two-Player Repeated Games." BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8683.

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Artificial intelligence (AI) agents will need to interact with both other AI agents and humans. One way to enable effective interaction is to create models of associates to help to predict the modeled agents' actions, plans, and intentions. If AI agents are able to predict what other agents in their environment will be doing in the future and can understand the intentions of these other agents, the AI agents can use these predictions in their planning, decision-making and assessing their own potential. Prior work [13, 14] introduced the S# algorithm, which is designed as a robust algorithm for many two-player repeated games (RGs) to enable cooperation among players. Because S# generates actions, has (internal) experts that seek to accomplish an internal intent, and associates plans with each expert, it is a useful algorithm for exploring intent, plan, and action in RGs. This thesis presents a graphical Bayesian model for predicting actions, plans, and intents of an S# agent. The same model is also used to predict human action. The actions, plans and intentions associated with each S# expert are (a) identified from the literature and (b) grouped by expert type. The Bayesian model then uses its transition probabilities to predict the action and expert type from observing human or S# play. Two techniques were explored for translating probability distributions into specific predictions: Maximum A Posteriori (MAP) and Aggregation approach. The Bayesian model was evaluated for three RGs (Prisoners Dilemma, Chicken and Alternator) as follows. Prediction accuracy of the model was compared to predictions from machine learning models (J48, Multi layer perceptron and Random Forest) as well as from the fixed strategies presented in [20]. Prediction accuracy was obtained by comparing the model's predictions against the actual player's actions. Accuracy for plan and intent prediction was measured by comparing predictions to the actual plans and intents followed by the S# agent. Since the plans and the intents of human players were not recorded in the dataset, this thesis does not measure the accuracy of the Bayesian model against actual human plans and intents. Results show that the Bayesian model effectively models the actions, plans, and intents of the S# algorithm across the various games. Additionally, the Bayesian model outperforms other methods for predicting human actions. When the games do not allow players to communicate using so-called “cheap talk”, the MAP-based predictions are significantly better than Aggregation-based predictions. There is no significant difference in the performance of MAP-based and Aggregation-based predictions for modeling human behavior when cheaptalk is allowed, except in the game of Chicken.
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Yu, Hong. "A data-driven approach for personalized drama management." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53851.

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An interactive narrative is a form of digital entertainment in which players can create or influence a dramatic storyline through actions, typically by assuming the role of a character in a fictional virtual world. The interactive narrative systems usually employ a drama manager (DM), an omniscient background agent that monitors the fictional world and determines what will happen next in the players' story experience. Prevailing approaches to drama management choose successive story plot points based on a set of criteria given by the game designers. In other words, the DM is a surrogate for the game designers. In this dissertation, I create a data-driven personalized drama manager that takes into consideration players' preferences. The personalized drama manager is capable of (1) modeling the players' preference over successive plot points from the players' feedback; (2) guiding the players towards selected plot points without sacrificing players' agency; (3) choosing target successive plot points that simultaneously increase the player's story preference ratings and the probability of the players selecting the plot points. To address the first problem, I develop a collaborative filtering algorithm that takes into account the specific sequence (or history) of experienced plot points when modeling players' preferences for future plot points. Unlike the traditional collaborative filtering algorithms that make one-shot recommendations of complete story artifacts (e.g., books, movies), the collaborative filtering algorithm I develop is a sequential recommendation algorithm that makes every successive recommendation based on all previous recommendations. To address the second problem, I create a multi-option branching story graph that allows multiple options to point to each plot point. The personalized DM working in the multi-option branching story graph can influence the players to make choices that coincide with the trajectories selected by the DM, while gives the players the full agency to make any selection that leads to any plot point in their own judgement. To address the third problem, the personalized DM models the probability that the players transitioning to each full-length stories and selects target stories that achieve the highest expected preference ratings at every branching point in the story space. The personalized DM is implemented in an interactive narrative system built with choose-your-own-adventure stories. Human study results show that the personalized DM can achieve significantly higher preference ratings than non-personalized DMs or DMs with pre-defined player types, while preserve the players' sense of agency.
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Correia, J. Steve. "Agent-based target detection in 3-dimensional environments." Thesis, Monterey, California. Naval Postgraduate School, 2005. http://hdl.handle.net/10945/2300.

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Visual perception modeling is generally weak for game AI and computer generated forces (CGF), or agents, in computer games and military simulations. Several tricks and shortcuts are used in perceptual modeling. The results are, under certain conditions, unrealistic behaviors that negatively effect user immersion in games and call into question the validity of calculations in fine resolution military simulations. By determining what the computer-generated agent sees using methods similar to that used to generate the human players' screen view in 3- D virtual environments, we hope to present a method that can more accurately model human visual perception, specifically the major problem of a entity "hiding in plain sight"
Lieutenant, United States Navy
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Vallim, Rosane Maria Maffei. "Mineração de fluxos contínuos de dados para jogos de computador." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-30082013-101303/.

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Um dos desafios da Inteligência Artificial aplicada em jogos é o aprendizado de comportamento, em que o objetivo é utilizar estatísticas obtidas da interação entre jogador e jogo de modo a reconhecer características particulares de um jogador ou monitorar a evolução de seu comportamento no decorrer do tempo. A maior parte dos trabalhos na área emprega modelos previamente aprendidos, por meio da utilização de algoritmos de Aprendizado de Máquina. Entretanto, são poucos os trabalhos que consideram que o comportamento de um jogador pode evoluir no tempo e que, portanto, reconhecer quando essas mudanças ocorrem é o primeiro passo para produzir jogos que se adaptam automaticamente às capacidades do jogador. Para detectar variações comportamentais em um jogador, são necessários algoritmos que processem dados de modo incremental. Esse pré-requisito motiva o estudo de algoritmos para detecção de mudanças da área de Mineração em Fluxos Contínuos de Dados. Entretanto, algumas das características dos algoritmos disponíveis na literatura inviabilizam sua aplicação direta ao problema de detecção de mudança em jogos. Visando contornar essas dificuldades, esta tese propõe duas novas abordagens para detecção de mudanças de comportamento. A primeira abordagem é baseada em um algoritmo incremental de agrupamento e detecção de novidades que é independente do número e formato dos grupos presentes nos dados e que utiliza um mecanismo de janela deslizante para detecção de mudanças de comportamento. A segunda abordagem, por outro lado, é baseada na comparação de janelas de tempo consecutivas utilizando espectrogramas gerados a partir dos dados contidos em cada janela. Os resultados experimentais utilizando simulações e dados de jogos comerciais indicam a aplicabilidade dos algoritmos propostos na tarefa de detecção de mudanças de comportamento de um jogador, assim como mostram sua vantagem em relação a outros algoritmos para detecção de mudança disponíveis na literatura
One of the challenges of Artificial Intelligence applied to games is behavior learning, where the objective is to use statistics derived from the interaction between the player and the game environment in order to recognize particular player characteristics or to monitor the evolution of a players behavior along time. The majority of work developed in this area applies models that were previously learned through the use of Machine Learning techniques. However, only a few pieces of work consider that the players behavior can evolve over time and, therefore, recognizing when behavior changes happen is the first step towards the production of games that adapt to the players needs. In order to detect changes in the behavior of a player, incremental algorithms are necessary, what motivates the study of change detection algorithms from the area of Data Stream Mining. However, some of the characteristics of the algorithms available in the literature make their application to the task of change detection in games unfeasible. To overcome these difficulties, this work proposes two new approaches for change detection. The first approach is based on an incremental clustering and novelty detection algorithm which is independent of the number and format of clusters and uses a mechanism for change detection based on sliding windows. The second approach, on the other hand, is based on the comparison of consecutive time windows using spectrograms created from the data inside each window. Experimental results using simulations and data from commercial games indicate the applicability of the proposed algorithms in the task of detecting a players changing behavior, as well as present their advantage when compared to other change detection algorithms available in the literature
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Books on the topic "Player modeling"

1

Binmore, K. G. Modeling rational players. London: London Schoolof Economics, 1986.

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Miller, Richard McDermott. Figure Sculpture in Wax and Plaster. Edited by Gloria Bley Miller. New York, USA: Dover Publications, 1987.

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Model-making: Materials and methods. Ramsbury: Crowood Press, 2008.

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Subduction: Insights from physical modeling. Dordrecht: Kluwer Academic Publishers, 1994.

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Clayton, Peirce. The clay lover's guide to making molds: Designing, making, using. Asheville, N.C: Lark Books, 1998.

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Govers, Rob. Dynamics of lithospheric extension: A modeling study. [Utrecht: Faculteit Aardwetenschappen der Rijksuniversiteit te Utrecht, 1993.

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Louis, Lions Jacques, ed. Modelling analysis and control of thin plates. Paris: Masson, 1988.

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Babeshko, Lyudmila, Mihail Bich, and Irina Orlova. Econometrics and econometric modeling. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1141216.

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The textbook covers a wide range of issues related to econometric modeling. Regression models are the core of econometric modeling, so the issues of their evaluation, testing of assumptions, adjustment and verification are given a significant place. Various aspects of multiple regression models are included: multicollinearity, dummy variables, and lag structure of variables. Methods of linearization and estimation of nonlinear models are considered. An apparatus for evaluating systems of simultaneous and apparently unrelated equations is presented. Attention is paid to time series models. Detailed solutions of the examples in Excel and the R software environment are included. Meets the requirements of the federal state educational standards of higher education of the latest generation. For undergraduate and graduate students studying in the field of "Economics", the curriculum of which includes the disciplines "Econometrics"," Econometric Modeling","Econometric research".
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Hodges, Dewey H. Modeling of composite beams and plates for static and dynamic analysis. Atlanta, Ga: School of Aerospace Engineering, Georgia Institute of Technology, 1990.

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United States. National Aeronautics and Space Administration., ed. Modeling of composite beams and plates for static and dynamic analysis. [Washington, DC: National Aeronautics and Space Administration, 1993.

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Book chapters on the topic "Player modeling"

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Farooq, Sehar Shahzad, and Kyung-Joong Kim. "Game Player Modeling." In Encyclopedia of Computer Graphics and Games, 1–5. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-08234-9_14-1.

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Bindewald, Jason M., Gilbert L. Peterson, and Michael E. Miller. "Clustering-Based Online Player Modeling." In Communications in Computer and Information Science, 86–100. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57969-6_7.

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Bindewald, Jason M., Gilbert L. Peterson, and Michael E. Miller. "Trajectory Generation with Player Modeling." In Advances in Artificial Intelligence, 42–49. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18356-5_4.

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Lankoski, Petri. "Modeling Player-Character Engagement in Single-Player Character-Driven Games." In Lecture Notes in Computer Science, 572–75. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03161-3_56.

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Lorenz, Ulf, and Tobias Tscheuschner. "Player Modeling, Search Algorithms and Strategies in Multi-player Games." In Lecture Notes in Computer Science, 210–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11922155_16.

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Yoon, Tae Bok, Dong Moon Kim, Kyo Hyeon Park, Jee Hyong Lee, and Kwan-Ho You. "Game Player Modeling Using D-FSMs." In Lecture Notes in Computer Science, 490–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73354-6_54.

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Missura, Olana, and Thomas Gärtner. "Player Modeling for Intelligent Difficulty Adjustment." In Discovery Science, 197–211. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04747-3_17.

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Hsieh, Yung-Huan, Shintami C. Hidayati, Wen-Huang Cheng, Min-Chun Hu, and Kai-Lung Hua. "Who’s the Best Charades Player? Mining Iconic Movement of Semantic Concepts." In MultiMedia Modeling, 231–41. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04114-8_20.

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Yannakakis, Georgios N., and Manolis Maragoudakis. "Player Modeling Impact on Player’s Entertainment in Computer Games." In User Modeling 2005, 74–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11527886_11.

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Buede, Dennis M., Paul J. Sticha, and Elise T. Axelrad. "Conversational Non-Player Characters for Virtual Training." In Social, Cultural, and Behavioral Modeling, 389–99. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39931-7_37.

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Conference papers on the topic "Player modeling"

1

Machado, Marlos C., Eduardo P. C. Fantini, and Luiz Chaimowicz. "Player modeling: Towards a common taxonomy." In Serious Games (CGAMES). IEEE, 2011. http://dx.doi.org/10.1109/cgames.2011.6000359.

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Yang, Lingfeng. "Modeling player performance in rhythm games." In ACM SIGGRAPH ASIA 2010 Sketches. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1899950.1899951.

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Smith, Adam M., Chris Lewis, Kenneth Hullet, and Anne Sullivan. "An inclusive view of player modeling." In the 6th International Conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2159365.2159419.

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Gray, Robert C., Jichen Zhu, Danielle Arigo, Evan Forman, and Santiago Ontañón. "Player Modeling via Multi-Armed Bandits." In FDG '20: International Conference on the Foundations of Digital Games. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3402942.3402952.

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Holmgard, Christoffer, Antonios Liapis, Julian Togelius, and Georgios N. Yannakakis. "Evolving personas for player decision modeling." In 2014 IEEE Conference on Computational Intelligence and Games (CIG). IEEE, 2014. http://dx.doi.org/10.1109/cig.2014.6932911.

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Romanoff, Chris, and Chris Romanoff. "Comanche Player Station - Comanche simulation in the Aviation Warfighting Cell." In Modeling and Simulation Technologies Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1997. http://dx.doi.org/10.2514/6.1997-3509.

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Carneiro, Emanuel Mineda, Adilson Marques da Cunha, and Luiz Alberto Vieira Dias. "Adaptive Game AI Architecture with Player Modeling." In 2014 Eleventh International Conference on Information Technology: New Generations (ITNG). IEEE, 2014. http://dx.doi.org/10.1109/itng.2014.40.

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Anagnostou, Kostas, and Manolis Maragoudakis. "Data Mining for Player Modeling in Videogames." In 2009 13th Panhellenic Conference on Informatics. IEEE, 2009. http://dx.doi.org/10.1109/pci.2009.28.

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Synnaeve, Gabriel, Pierre Bessière, Ali Mohammad-Djafari, Jean-François Bercher, and Pierre Bessiére. "Bayesian Modeling of a Human MMORPG Player." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: Proceedings of the 30th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. AIP, 2011. http://dx.doi.org/10.1063/1.3573658.

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Pedersen, Chris, Julian Togelius, and Georgios N. Yannakakis. "Modeling player experience in Super Mario Bros." In 2009 IEEE Symposium on Computational Intelligence and Games (CIG). IEEE, 2009. http://dx.doi.org/10.1109/cig.2009.5286482.

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Reports on the topic "Player modeling"

1

Trinh, K. V. Modeling the in-plane tension failure of composite plates. Office of Scientific and Technical Information (OSTI), November 1997. http://dx.doi.org/10.2172/563207.

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Babuska, I., and L. Li. Hierarchic Modeling of Plates. Fort Belvoir, VA: Defense Technical Information Center, December 1990. http://dx.doi.org/10.21236/ada232754.

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Andrews, Sydney. Chemical Condensation During Planet Formation: Modeling Parameters. Office of Scientific and Technical Information (OSTI), July 2014. http://dx.doi.org/10.2172/1148970.

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Rensink, M. E., and T. D. Rognlien. Modeling impurities and tilted plates in the ITER divertor. Office of Scientific and Technical Information (OSTI), July 1996. http://dx.doi.org/10.2172/371415.

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Celmins, Aivars K. Fuzzy Modeling of Armor Plate Bending by Blast. Fort Belvoir, VA: Defense Technical Information Center, August 1990. http://dx.doi.org/10.21236/ada226388.

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Tatlicioglu, E., Ian D. Walker, and Darren M. Dawson. Dynamic Modelling for Planar Extensible Continuum Robot Manipulators. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada462495.

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Battaile, Corbett Chandler, Harry K. Moffat, Amy Cha-Tien Sun, David George Enos, Lysle M. Serna, and Neil Robert Sorensen. Modeling pore corrosion in normally open gold- plated copper connectors. Office of Scientific and Technical Information (OSTI), September 2008. http://dx.doi.org/10.2172/942183.

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Petravic, M. Modeling of ultra-high recycling divertors with the PLANET code. Office of Scientific and Technical Information (OSTI), July 1993. http://dx.doi.org/10.2172/10176221.

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Abboud, Alexander. Modeling of Radiolytic Hydrogen Generation of Irradiated Surrogate Aluminum Plates. Office of Scientific and Technical Information (OSTI), March 2022. http://dx.doi.org/10.2172/1924440.

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Freeman, Janine, Jonathan Whitmore, Leah Kaffine, Nate Blair, and Aron P. Dobos. System Advisor Model: Flat Plate Photovoltaic Performance Modeling Validation Report. Office of Scientific and Technical Information (OSTI), December 2013. http://dx.doi.org/10.2172/1115788.

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