Pub Date : 2013-10-17DOI: 10.1109/CIG.2013.6633645
Pu Yang, D. Roberts
When doing post-competition analysis in team games, it can be hard to figure out if a team members' character attribute development has been successful directly from game logs. Additionally, it can also be hard to figure out how the performance of one team member affects the performance of another. In this paper, we present a data-driven method for automatically discovering patterns in successful team members' character attribute development in team games. We first represent team members' character attribute development using time series of informative attributes. We then find the thresholds to separate fast and slow attribute growth rates using clustering and linear regression. We create a set of categorical attribute growth rates by comparing against the thresholds. If the growth rate is greater than the threshold it is categorized as fast growth rate; if the growth rate is less than the threshold it is categorized as slow growth rate. After obtaining the set of categorical attribute growth rates, we build a decision tree on the set. Finally, we characterize the patterns of team success in terms of rules which describe team members' character attribute growth rates. We present an evaluation of our methodology on three real games: DotA,1 Warcraft III,2 and Starcraft II.3 A standard machine-learning-style evaluation of the experimental results shows the discovered patterns are highly related to successful team strategies and achieve an average 86% prediction accuracy when testing on new game logs.
{"title":"Knowledge discovery for characterizing team success or failure in (A)RTS games","authors":"Pu Yang, D. Roberts","doi":"10.1109/CIG.2013.6633645","DOIUrl":"https://doi.org/10.1109/CIG.2013.6633645","url":null,"abstract":"When doing post-competition analysis in team games, it can be hard to figure out if a team members' character attribute development has been successful directly from game logs. Additionally, it can also be hard to figure out how the performance of one team member affects the performance of another. In this paper, we present a data-driven method for automatically discovering patterns in successful team members' character attribute development in team games. We first represent team members' character attribute development using time series of informative attributes. We then find the thresholds to separate fast and slow attribute growth rates using clustering and linear regression. We create a set of categorical attribute growth rates by comparing against the thresholds. If the growth rate is greater than the threshold it is categorized as fast growth rate; if the growth rate is less than the threshold it is categorized as slow growth rate. After obtaining the set of categorical attribute growth rates, we build a decision tree on the set. Finally, we characterize the patterns of team success in terms of rules which describe team members' character attribute growth rates. We present an evaluation of our methodology on three real games: DotA,1 Warcraft III,2 and Starcraft II.3 A standard machine-learning-style evaluation of the experimental results shows the discovered patterns are highly related to successful team strategies and achieve an average 86% prediction accuracy when testing on new game logs.","PeriodicalId":158902,"journal":{"name":"2013 IEEE Conference on Computational Inteligence in Games (CIG)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124706437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-08-01DOI: 10.1109/CIG.2013.6633635
J. A. Brown
Multiple Agent Genetic Networks (MAGnet) are spatially structured evolutionary algorithms which move both evolving agents as well as instances of a problem about a combinatorial graph. Previous work has examined their use on the Iterated Prisoner's Dilemma, a well known non-zero sum game, in order for classification of agent types based on behaviours. Only a small complete graph was examined. In this study, a larger set of graphs with thirty-two nodes are examined. The graphs examined are: a cycle graph, two Peterson graphs with differing internal rings, a hypercube in five dimensions, and the complete graph. These graphs and properties are examined for a number of canonical agents, as well as a few interesting types which involve handshaking. It was found that the MAGnet system produces a similar classification as the smaller graph when the connectivity within the graph is high. Lower graph connectivity leads to a process by which disjoint subgraphs can be formed; this is based on the method of evolution causing a subpopulation collapse in which the number of problems on a node tends to zero and the node is removed.
{"title":"Examination of graphs in Multiple Agent Genetic Networks for Iterated Prisoner's Dilemma","authors":"J. A. Brown","doi":"10.1109/CIG.2013.6633635","DOIUrl":"https://doi.org/10.1109/CIG.2013.6633635","url":null,"abstract":"Multiple Agent Genetic Networks (MAGnet) are spatially structured evolutionary algorithms which move both evolving agents as well as instances of a problem about a combinatorial graph. Previous work has examined their use on the Iterated Prisoner's Dilemma, a well known non-zero sum game, in order for classification of agent types based on behaviours. Only a small complete graph was examined. In this study, a larger set of graphs with thirty-two nodes are examined. The graphs examined are: a cycle graph, two Peterson graphs with differing internal rings, a hypercube in five dimensions, and the complete graph. These graphs and properties are examined for a number of canonical agents, as well as a few interesting types which involve handshaking. It was found that the MAGnet system produces a similar classification as the smaller graph when the connectivity within the graph is high. Lower graph connectivity leads to a process by which disjoint subgraphs can be formed; this is based on the method of evolution causing a subpopulation collapse in which the number of problems on a node tends to zero and the node is removed.","PeriodicalId":158902,"journal":{"name":"2013 IEEE Conference on Computational Inteligence in Games (CIG)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132217029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-08-01DOI: 10.1109/CIG.2013.6633656
Christopher A. Ballinger, S. Louis
Finding robust solutions that are also capable of beating specific opponents presents a challenging problem. This paper investigates solving this problem by using case-injection with a coevolutionary algorithm. Specifically, we recorded winning strategies used by a human player against a coevolved strategy and then injected the player's strategies into the coevolutionary teachset. We compare the strategies produced by case-injected coevolution to strategies produced by a genetic algorithm that only evaluated against the player's strategies. In this paper, our results show that genetic algorithms do not work well against sufficiently difficult opponents. However, coevolution eventually learns to defeat these opponents by first bootstrapping strategies that work well in general, which drives the population closer to strategies that can defeat the challenging opponent. This work informs our research on finding robust real-time strategy game players that also defeat specific opponents.
{"title":"Finding robust strategies to defeat specific opponents using case-injected coevolution","authors":"Christopher A. Ballinger, S. Louis","doi":"10.1109/CIG.2013.6633656","DOIUrl":"https://doi.org/10.1109/CIG.2013.6633656","url":null,"abstract":"Finding robust solutions that are also capable of beating specific opponents presents a challenging problem. This paper investigates solving this problem by using case-injection with a coevolutionary algorithm. Specifically, we recorded winning strategies used by a human player against a coevolved strategy and then injected the player's strategies into the coevolutionary teachset. We compare the strategies produced by case-injected coevolution to strategies produced by a genetic algorithm that only evaluated against the player's strategies. In this paper, our results show that genetic algorithms do not work well against sufficiently difficult opponents. However, coevolution eventually learns to defeat these opponents by first bootstrapping strategies that work well in general, which drives the population closer to strategies that can defeat the challenging opponent. This work informs our research on finding robust real-time strategy game players that also defeat specific opponents.","PeriodicalId":158902,"journal":{"name":"2013 IEEE Conference on Computational Inteligence in Games (CIG)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133244685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-08-01DOI: 10.1109/CIG.2013.6633658
E. Powley, D. Whitehouse, P. Cowling
This paper describes our entry to the Multiobjective Physical Travelling Salesman Problem (MO-PTSP) competition at the IEEE CIG 2013 conference. MO-PTSP combines the classical Travelling Salesman Problem with the task of steering a simulated spaceship on the 2-D plane, requiring that the controller minimises the three objectives of time taken, fuel consumed and damage incurred. Our entry to the MO-PTSP competition builds upon our winning entry to the previous (single-objective) PTSP competitions. This controller consists of two key components: a pre-planning stage using a classical TSP solver with a path cost measure that takes the physics of the problem into account, and a steering controller using Monte Carlo Tree Search (MCTS) with macro-actions (repeated actions), depth limiting and a heuristic fitness function for nonterminal states. We demonstrate that by modifying the two fitness functions we can produce effective behaviour in MO-PTSP without the need for major modifications to the overall architecture. The fitness functions used by our controller have several parameters, which must be set to ensure the best performance. Given the number of parameters and the difficulty of optimising a controller to satisfy multiple objectives in a search space which is many orders of magnitude larger than that encountered in a turn-based game such as Go, we show that informed hand tuning of parameters is insufficient for this task. We present an automatic parameter tuning method using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm, which produced parameter settings that dominate our hand tuned parameters. Additionally we show that the robustness of the controller using hand tuned parameters can be improved by detecting when the controller is trapped in a poor quality local optimum and escaping by switching to an alternate fitness function.
{"title":"Monte Carlo Tree Search with macro-actions and heuristic route planning for the Multiobjective Physical Travelling Salesman Problem","authors":"E. Powley, D. Whitehouse, P. Cowling","doi":"10.1109/CIG.2013.6633658","DOIUrl":"https://doi.org/10.1109/CIG.2013.6633658","url":null,"abstract":"This paper describes our entry to the Multiobjective Physical Travelling Salesman Problem (MO-PTSP) competition at the IEEE CIG 2013 conference. MO-PTSP combines the classical Travelling Salesman Problem with the task of steering a simulated spaceship on the 2-D plane, requiring that the controller minimises the three objectives of time taken, fuel consumed and damage incurred. Our entry to the MO-PTSP competition builds upon our winning entry to the previous (single-objective) PTSP competitions. This controller consists of two key components: a pre-planning stage using a classical TSP solver with a path cost measure that takes the physics of the problem into account, and a steering controller using Monte Carlo Tree Search (MCTS) with macro-actions (repeated actions), depth limiting and a heuristic fitness function for nonterminal states. We demonstrate that by modifying the two fitness functions we can produce effective behaviour in MO-PTSP without the need for major modifications to the overall architecture. The fitness functions used by our controller have several parameters, which must be set to ensure the best performance. Given the number of parameters and the difficulty of optimising a controller to satisfy multiple objectives in a search space which is many orders of magnitude larger than that encountered in a turn-based game such as Go, we show that informed hand tuning of parameters is insufficient for this task. We present an automatic parameter tuning method using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm, which produced parameter settings that dominate our hand tuned parameters. Additionally we show that the robustness of the controller using hand tuned parameters can be improved by detecting when the controller is trapped in a poor quality local optimum and escaping by switching to an alternate fitness function.","PeriodicalId":158902,"journal":{"name":"2013 IEEE Conference on Computational Inteligence in Games (CIG)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125754218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-08-01DOI: 10.1109/CIG.2013.6633618
Mihai Polceanu
Believability of automated characters in virtual worlds has posed a challenge for many years. In this paper, the author discusses a novel approach of using human-inspired mirroring behavior in MirrorBot, an Unreal Tournament 2004 game bot which crossed the humanness barrier and won the 2K BotPrize 2012 competition with the score of 52.2%, a record in the five year history of this contest. A comparison with past contest entries is presented and the relevance of the mirroring behavior as a humanness improvement factor is argued. The modules that compose MirrorBot's architecture are presented along with a discussion of the advantages of this approach and proposed solutions for its drawbacks. The contribution continues with a discussion of the bot's results in humanness and judging accuracy.
{"title":"MirrorBot: Using human-inspired mirroring behavior to pass a turing test","authors":"Mihai Polceanu","doi":"10.1109/CIG.2013.6633618","DOIUrl":"https://doi.org/10.1109/CIG.2013.6633618","url":null,"abstract":"Believability of automated characters in virtual worlds has posed a challenge for many years. In this paper, the author discusses a novel approach of using human-inspired mirroring behavior in MirrorBot, an Unreal Tournament 2004 game bot which crossed the humanness barrier and won the 2K BotPrize 2012 competition with the score of 52.2%, a record in the five year history of this contest. A comparison with past contest entries is presented and the relevance of the mirroring behavior as a humanness improvement factor is argued. The modules that compose MirrorBot's architecture are presented along with a discussion of the advantages of this approach and proposed solutions for its drawbacks. The contribution continues with a discussion of the bot's results in humanness and judging accuracy.","PeriodicalId":158902,"journal":{"name":"2013 IEEE Conference on Computational Inteligence in Games (CIG)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116880515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-08-01DOI: 10.1109/CIG.2013.6633641
S. A. Roberts, S. Lucas
This paper investigates the relationship between the difficulty and the interestingness of individual problem candidates from within a class of related problems, using Lunar Lander as a case study. In this class of problems, a 2D spaceship must be controlled by a simple set of macro-actions, including both linear and angular impulses, such that it fulfils a set of weighted criteria relating to landing on a jagged landscape with flat landing pads. It is demonstrated that a very simple measure based on standard deviations of improvement can be used to guide evolution to develop interesting problems in this class of problems, which in turn can be solved using evolution strategies to get a high level of improvement based on initial random performance. We examine the impact of the measure used on the evolution of the problems, and also what aspects of this problem class affect the difficulty and interestingness the most.
{"title":"Measuring interestingness of continuous game problems","authors":"S. A. Roberts, S. Lucas","doi":"10.1109/CIG.2013.6633641","DOIUrl":"https://doi.org/10.1109/CIG.2013.6633641","url":null,"abstract":"This paper investigates the relationship between the difficulty and the interestingness of individual problem candidates from within a class of related problems, using Lunar Lander as a case study. In this class of problems, a 2D spaceship must be controlled by a simple set of macro-actions, including both linear and angular impulses, such that it fulfils a set of weighted criteria relating to landing on a jagged landscape with flat landing pads. It is demonstrated that a very simple measure based on standard deviations of improvement can be used to guide evolution to develop interesting problems in this class of problems, which in turn can be solved using evolution strategies to get a high level of improvement based on initial random performance. We examine the impact of the measure used on the evolution of the problems, and also what aspects of this problem class affect the difficulty and interestingness the most.","PeriodicalId":158902,"journal":{"name":"2013 IEEE Conference on Computational Inteligence in Games (CIG)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116249724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-08-01DOI: 10.1109/CIG.2013.6633655
David Buckley, Ke Chen, Joshua D. Knowles
One way to make video games more attractive to a wider audience is to make them adaptive to players. The preferences and skills of players can be determined in a variety of ways, but should be done as unobtrusively as possible to keep the player immersed. This paper explores how gameplay input recorded in a first-person shooter can predict a player's ability. As these features were able to model a player's skill with 76% accuracy, without the use of game-specific features, we believe their use would be transferable across similar games within the genre.
{"title":"Predicting skill from gameplay input to a first-person shooter","authors":"David Buckley, Ke Chen, Joshua D. Knowles","doi":"10.1109/CIG.2013.6633655","DOIUrl":"https://doi.org/10.1109/CIG.2013.6633655","url":null,"abstract":"One way to make video games more attractive to a wider audience is to make them adaptive to players. The preferences and skills of players can be determined in a variety of ways, but should be done as unobtrusively as possible to keep the player immersed. This paper explores how gameplay input recorded in a first-person shooter can predict a player's ability. As these features were able to model a player's skill with 76% accuracy, without the use of game-specific features, we believe their use would be transferable across similar games within the genre.","PeriodicalId":158902,"journal":{"name":"2013 IEEE Conference on Computational Inteligence in Games (CIG)","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124287438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-08-01DOI: 10.1109/CIG.2013.6633636
Chong-U Lim, D. Harrell
Game players express their values related to self-expression through various means such as avatar customization, gameplay styles, and interactions with other players. Multiplayer online games, now often integrated with social networks, provide social contexts in which player-to-player interactions take place, for example, through the trading of virtual items between players. Building upon a theoretical framework based in computer science and cognitive science, we present results from a novel approach to modeling and analyzing player values in terms of both preferences made in avatar customization, and patterns in social networking use. Our approach resulted in the development of the Steam-Player-Preference Analyzer (Steam-PPA) system, which (1) performs advanced data collection on publicly available social networking profile information and (2) the AIR Toolkit Status Performance Classifier (AIR-SPC), which uses machine learning techniques including clustering, natural language processing, and support vector machines (SVM) to perform inference on the data. As an initial case-study, we apply both systems to the popular, and commercially successful, multi-player first-person-shooter game Team Fortress 2 by analyzing information from player accounts on the social network Steam, together with avatar customization information generated by the player within the game. Our model uses social networking information to predict the likelihood of players customizing their profile in several ways associated with the monetary values of the players' avatar.
{"title":"Modeling player preferences in avatar customization using social network data: A case-study using virtual items in Team Fortress 2","authors":"Chong-U Lim, D. Harrell","doi":"10.1109/CIG.2013.6633636","DOIUrl":"https://doi.org/10.1109/CIG.2013.6633636","url":null,"abstract":"Game players express their values related to self-expression through various means such as avatar customization, gameplay styles, and interactions with other players. Multiplayer online games, now often integrated with social networks, provide social contexts in which player-to-player interactions take place, for example, through the trading of virtual items between players. Building upon a theoretical framework based in computer science and cognitive science, we present results from a novel approach to modeling and analyzing player values in terms of both preferences made in avatar customization, and patterns in social networking use. Our approach resulted in the development of the Steam-Player-Preference Analyzer (Steam-PPA) system, which (1) performs advanced data collection on publicly available social networking profile information and (2) the AIR Toolkit Status Performance Classifier (AIR-SPC), which uses machine learning techniques including clustering, natural language processing, and support vector machines (SVM) to perform inference on the data. As an initial case-study, we apply both systems to the popular, and commercially successful, multi-player first-person-shooter game Team Fortress 2 by analyzing information from player accounts on the social network Steam, together with avatar customization information generated by the player within the game. Our model uses social networking information to predict the likelihood of players customizing their profile in several ways associated with the monetary values of the players' avatar.","PeriodicalId":158902,"journal":{"name":"2013 IEEE Conference on Computational Inteligence in Games (CIG)","volume":"156 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133410521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-08-01DOI: 10.1109/CIG.2013.6633666
Ho-Chul Cho, Kyung-Joong Kim, Sung-Bae Cho
StarCraft is a real-time strategy (RTS) game and the choice of strategy has big impact on the final results of the game. For human players, the most important thing in the game is to select the strategy in the early stage of the game. Also, it is important to recognize the opponent's strategy as quickly as possible. Because of the “fog-of-war” in the game, the player should send a scouting unit to opponent's hidden territory and the player predicts the types of strategy from the partially observed information. Usually, expert players are familiar with the relationships between two build orders and they can change the current build order if his choice is not strong to the opponent's strategy. However, players in AI competitions show quite different behaviors compared to the human leagues. For example, they usually have a pre-selected build order and rarely change their order during the game. In fact, the computer players have little interest in recognizing opponent's strategy and scouting units are used in a limited manner. The reason is that the implementation of scouting behavior and the change of build order from the scouting vision is not a trivial problem. In this paper, we propose to use replays to predict the strategy of players and make decision on the change of build orders. Experimental results on the public replay files show that the proposed method predicts opponent's strategy accurately and increases the chance of winning in the game.
{"title":"Replay-based strategy prediction and build order adaptation for StarCraft AI bots","authors":"Ho-Chul Cho, Kyung-Joong Kim, Sung-Bae Cho","doi":"10.1109/CIG.2013.6633666","DOIUrl":"https://doi.org/10.1109/CIG.2013.6633666","url":null,"abstract":"StarCraft is a real-time strategy (RTS) game and the choice of strategy has big impact on the final results of the game. For human players, the most important thing in the game is to select the strategy in the early stage of the game. Also, it is important to recognize the opponent's strategy as quickly as possible. Because of the “fog-of-war” in the game, the player should send a scouting unit to opponent's hidden territory and the player predicts the types of strategy from the partially observed information. Usually, expert players are familiar with the relationships between two build orders and they can change the current build order if his choice is not strong to the opponent's strategy. However, players in AI competitions show quite different behaviors compared to the human leagues. For example, they usually have a pre-selected build order and rarely change their order during the game. In fact, the computer players have little interest in recognizing opponent's strategy and scouting units are used in a limited manner. The reason is that the implementation of scouting behavior and the change of build order from the scouting vision is not a trivial problem. In this paper, we propose to use replays to predict the strategy of players and make decision on the change of build orders. Experimental results on the public replay files show that the proposed method predicts opponent's strategy accurately and increases the chance of winning in the game.","PeriodicalId":158902,"journal":{"name":"2013 IEEE Conference on Computational Inteligence in Games (CIG)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122904346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-08-01DOI: 10.1109/CIG.2013.6633616
S. Tekofsky, P. Spronck, A. Plaat, Jaap van den Herik, J. Broersen
Age has been shown to influence our preferences, choices, and cognitive performance. We expect this influence to be visible in the play style of an individual. Player models would then benefit from incorporating age, allowing developers to offer an increasingly personalized game experience to the player. To investigate the relationship between age and play style, we set out to determine how much of the variance in a player's age can be explained by his play style. For this purpose, we used the data from a survey (`PsyOps') among 13,376 `Battlefield 3' players. Starting out with 60 play style variables, we found that 45.7% of the variance in age can be explained by 46 play style variables. Furthermore, similar percentages of variance in age are explained when the sample is divided along gaming platform: 31 play style variables explain 43.1% on PC; 30 play style variables explain 53.9% on Xbox 360; 28 play style variables explain 51.7% on Playstation 3. Our findings have a high external validity due to the large and heterogeneous nature of the sample. The strength of the relationship between age and play style is considered `large' according to Cohen's classification. Previous research indicates that the nature of the relationship between age and play style is likely to be based on life-time developments in cognitive performance, motivation, and personality. All in all, our findings merit a recommendation to incorporate age in future player models.
{"title":"Play style: Showing your age","authors":"S. Tekofsky, P. Spronck, A. Plaat, Jaap van den Herik, J. Broersen","doi":"10.1109/CIG.2013.6633616","DOIUrl":"https://doi.org/10.1109/CIG.2013.6633616","url":null,"abstract":"Age has been shown to influence our preferences, choices, and cognitive performance. We expect this influence to be visible in the play style of an individual. Player models would then benefit from incorporating age, allowing developers to offer an increasingly personalized game experience to the player. To investigate the relationship between age and play style, we set out to determine how much of the variance in a player's age can be explained by his play style. For this purpose, we used the data from a survey (`PsyOps') among 13,376 `Battlefield 3' players. Starting out with 60 play style variables, we found that 45.7% of the variance in age can be explained by 46 play style variables. Furthermore, similar percentages of variance in age are explained when the sample is divided along gaming platform: 31 play style variables explain 43.1% on PC; 30 play style variables explain 53.9% on Xbox 360; 28 play style variables explain 51.7% on Playstation 3. Our findings have a high external validity due to the large and heterogeneous nature of the sample. The strength of the relationship between age and play style is considered `large' according to Cohen's classification. Previous research indicates that the nature of the relationship between age and play style is likely to be based on life-time developments in cognitive performance, motivation, and personality. All in all, our findings merit a recommendation to incorporate age in future player models.","PeriodicalId":158902,"journal":{"name":"2013 IEEE Conference on Computational Inteligence in Games (CIG)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131512721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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