Pub Date : 2016-09-01DOI: 10.1109/CIG.2016.7860432
Philipp Beau, S. Bakkes
Designing a (video) game such that it is balanced — i.e. fair for all players — is a prevailing challenge in game design. Perhaps counter-intuitively, games that are symmetric with respect to (board) design, starting conditions, and the employed action set, are not necessarily fair games. Indeed, perfect play from all players does not automatically lead to a draw, but may probabilistically favour e.g., the first player to move. Even more so, asymmetric games — in which the action set of one player is typically highly distinct from that of another player — are generally unbalanced unless meticulous care has been taken to ensure that the asymmetry in the design does not skew win probabilities. In this context, the present paper contributes a method for automatically balancing the design of asymmetric games. It employs Monte Carlo simulation to analyse the relative impact of game actions, and iteratively adjusts attributes of the game actions till the game design is balanced by approximation. To assess the effectiveness of the proposed method, experiments were performed with automatically balancing a set of tower-defence games. Preliminary experimental results revealed that the proposed method (1) is able to identify the principal component of a game's imbalance, and (2) can automatically adjust the game design till it is balanced by approximation.
{"title":"Automated game balancing of asymmetric video games","authors":"Philipp Beau, S. Bakkes","doi":"10.1109/CIG.2016.7860432","DOIUrl":"https://doi.org/10.1109/CIG.2016.7860432","url":null,"abstract":"Designing a (video) game such that it is balanced — i.e. fair for all players — is a prevailing challenge in game design. Perhaps counter-intuitively, games that are symmetric with respect to (board) design, starting conditions, and the employed action set, are not necessarily fair games. Indeed, perfect play from all players does not automatically lead to a draw, but may probabilistically favour e.g., the first player to move. Even more so, asymmetric games — in which the action set of one player is typically highly distinct from that of another player — are generally unbalanced unless meticulous care has been taken to ensure that the asymmetry in the design does not skew win probabilities. In this context, the present paper contributes a method for automatically balancing the design of asymmetric games. It employs Monte Carlo simulation to analyse the relative impact of game actions, and iteratively adjusts attributes of the game actions till the game design is balanced by approximation. To assess the effectiveness of the proposed method, experiments were performed with automatically balancing a set of tower-defence games. Preliminary experimental results revealed that the proposed method (1) is able to identify the principal component of a game's imbalance, and (2) can automatically adjust the game design till it is balanced by approximation.","PeriodicalId":6594,"journal":{"name":"2016 IEEE Conference on Computational Intelligence and Games (CIG)","volume":"ahead-of-print 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78946114","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 : 2016-09-01DOI: 10.1109/CIG.2016.7860396
Daniel Karavolos, Antonios Liapis, Georgios N. Yannakakis
This paper describes a search-based generative method which creates game levels by evolving the intended sequence of player actions rather than their spatial layout. The proposed approach evolves graphs where nodes representing player actions are linked to form one or more ways in which a mission can be completed. Initially simple graphs containing the mission's starting and ending nodes are evolved via mutation operators which expand and prune the graph topology. Evolution is guided by several objective functions which capture game design patterns such as exploration or balance; experiments in this paper explore how these objective functions and their combinations affect the quality and diversity of the evolved mission graphs.
{"title":"Evolving missions to create game spaces","authors":"Daniel Karavolos, Antonios Liapis, Georgios N. Yannakakis","doi":"10.1109/CIG.2016.7860396","DOIUrl":"https://doi.org/10.1109/CIG.2016.7860396","url":null,"abstract":"This paper describes a search-based generative method which creates game levels by evolving the intended sequence of player actions rather than their spatial layout. The proposed approach evolves graphs where nodes representing player actions are linked to form one or more ways in which a mission can be completed. Initially simple graphs containing the mission's starting and ending nodes are evolved via mutation operators which expand and prune the graph topology. Evolution is guided by several objective functions which capture game design patterns such as exploration or balance; experiments in this paper explore how these objective functions and their combinations affect the quality and diversity of the evolved mission graphs.","PeriodicalId":6594,"journal":{"name":"2016 IEEE Conference on Computational Intelligence and Games (CIG)","volume":"77 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82251610","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 : 2016-09-01DOI: 10.1109/CIG.2016.7860390
Byung-Hak Yoo, Kyung-Joong Kim
Recently, procedural content generation (PCG) has attracted positive attentions from gamers and applied for various content types such as maps, items and so on. Deep neural networks have been reported that they have potential to learn styles of artistic images. In this study, we propose to apply convolutional neural networks to change artistic styles of video game graphics. It's expected to change original games into different styles (modern, old-fashioned, scientific, and so on) given the input images. We applied the neural styling algorithm to the game images from Hedgewars, an open-source turn-based strategy game. Our results show that styles of video games can be changed from an input styling image.
{"title":"Changing video game graphic styles using neural algorithms","authors":"Byung-Hak Yoo, Kyung-Joong Kim","doi":"10.1109/CIG.2016.7860390","DOIUrl":"https://doi.org/10.1109/CIG.2016.7860390","url":null,"abstract":"Recently, procedural content generation (PCG) has attracted positive attentions from gamers and applied for various content types such as maps, items and so on. Deep neural networks have been reported that they have potential to learn styles of artistic images. In this study, we propose to apply convolutional neural networks to change artistic styles of video game graphics. It's expected to change original games into different styles (modern, old-fashioned, scientific, and so on) given the input images. We applied the neural styling algorithm to the game images from Hedgewars, an open-source turn-based strategy game. Our results show that styles of video games can be changed from an input styling image.","PeriodicalId":6594,"journal":{"name":"2016 IEEE Conference on Computational Intelligence and Games (CIG)","volume":"33 1","pages":"1-2"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80253226","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 : 2016-09-01DOI: 10.1109/CIG.2016.7860422
J. Schmitt, H. Köstler
The goal of this work is to develop a multi-objective genetic algorithm for simulating optimal fights between arbitrary units in the real-time strategy game StarCraft II. As there is no freely available application programming interface for controlling units in the game directly, this first requires an accurate simulation of the actual game mechanics. Next, based on the concept of artificial potential fields a general behavior model is developed which allows controlling units in an optimal way based on a number of real-valued parameters. The goal of each individual unit is to maximize their damage output while minimizing the amount of received damage. Finding parameter values that control the units of two opposing players in an optimal way with respect to these objectives can be formulated as a multi-objective continuous optimization problem. This problem is then solved by applying a genetic algorithm that optimizes the behavior of each unit of two opposing players in a competitive way. To evaluate the quality of a solution, only a finite number of solutions of the opponent can be used. Therefore, the current optima are repeatedly exchanged between both players and serve as input for the simulated encounter. By comparing the solutions of both players at the end of the optimization, it can be estimated if one of the two players has an advantage. Finally, in order to evaluate the effectiveness of the presented approach, a number of sample build orders, which correspond to the amount of units that have been produced until a certain point of time, serve as input for several optimization runs.
{"title":"A multi-objective genetic algorithm for simulating optimal fights in StarCraft II","authors":"J. Schmitt, H. Köstler","doi":"10.1109/CIG.2016.7860422","DOIUrl":"https://doi.org/10.1109/CIG.2016.7860422","url":null,"abstract":"The goal of this work is to develop a multi-objective genetic algorithm for simulating optimal fights between arbitrary units in the real-time strategy game StarCraft II. As there is no freely available application programming interface for controlling units in the game directly, this first requires an accurate simulation of the actual game mechanics. Next, based on the concept of artificial potential fields a general behavior model is developed which allows controlling units in an optimal way based on a number of real-valued parameters. The goal of each individual unit is to maximize their damage output while minimizing the amount of received damage. Finding parameter values that control the units of two opposing players in an optimal way with respect to these objectives can be formulated as a multi-objective continuous optimization problem. This problem is then solved by applying a genetic algorithm that optimizes the behavior of each unit of two opposing players in a competitive way. To evaluate the quality of a solution, only a finite number of solutions of the opponent can be used. Therefore, the current optima are repeatedly exchanged between both players and serve as input for the simulated encounter. By comparing the solutions of both players at the end of the optimization, it can be estimated if one of the two players has an advantage. Finally, in order to evaluate the effectiveness of the presented approach, a number of sample build orders, which correspond to the amount of units that have been produced until a certain point of time, serve as input for several optimization runs.","PeriodicalId":6594,"journal":{"name":"2016 IEEE Conference on Computational Intelligence and Games (CIG)","volume":"35 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77870435","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 : 2016-09-01DOI: 10.1109/CIG.2016.7860416
Elie Bursztein
In this paper, we demonstrate the feasibility of a competitive player using statistical learning methods to gain an edge while playing a collectible card game (CCG) online. We showcase how our attacks work in practice against the most popular online CCG, Hearthstone: Heroes of World of Warcraft, which had over 50 million players as of April 2016. Like online poker, the large and regular cash prizes of Hearthstone's online tournaments make it a prime target for cheaters in search of a quick score. As of 2016, over $3,000,000 in prize money has been distributed in tournaments, and the best players earned over $10,000 from purely online tournaments. In this paper, we present the first algorithm that is able to learn and exploit the structure of card decks to predict with very high accuracy which cards an opponent will play in future turns. We evaluate it on real Hearthstone games and show that at its peak, between turns three and five of a game, this algorithm is able to predict the most probable future card with an accuracy above 95%. This attack was called “game breaking” by Blizzard, the creator of Hearthstone.
{"title":"I am a legend: Hacking hearthstone using statistical learning methods","authors":"Elie Bursztein","doi":"10.1109/CIG.2016.7860416","DOIUrl":"https://doi.org/10.1109/CIG.2016.7860416","url":null,"abstract":"In this paper, we demonstrate the feasibility of a competitive player using statistical learning methods to gain an edge while playing a collectible card game (CCG) online. We showcase how our attacks work in practice against the most popular online CCG, Hearthstone: Heroes of World of Warcraft, which had over 50 million players as of April 2016. Like online poker, the large and regular cash prizes of Hearthstone's online tournaments make it a prime target for cheaters in search of a quick score. As of 2016, over $3,000,000 in prize money has been distributed in tournaments, and the best players earned over $10,000 from purely online tournaments. In this paper, we present the first algorithm that is able to learn and exploit the structure of card decks to predict with very high accuracy which cards an opponent will play in future turns. We evaluate it on real Hearthstone games and show that at its peak, between turns three and five of a game, this algorithm is able to predict the most probable future card with an accuracy above 95%. This attack was called “game breaking” by Blizzard, the creator of Hearthstone.","PeriodicalId":6594,"journal":{"name":"2016 IEEE Conference on Computational Intelligence and Games (CIG)","volume":"74 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84932695","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 : 2016-09-01DOI: 10.1109/CIG.2016.7860420
M. Mozgovoy, Marina Purgina, I. Umarov
We describe a method used to build a practical AI system for a mobile game of tennis. The chosen approach had to support two goals: (1) provide a large number of believable and diverse AI characters, and (2) let the users train AI “ghost” characters able to substitute them. We achieve these goals by learning AI agents from collected behavior data of human-controlled characters. The acquired knowledge is used by a case-based reasoning algorithm to perform human-like decision making. Our experiments show that the resulting agents indeed exhibit a variety of recognizable play styles, resembling the play styles of their human trainers. The resulting AI system demonstrated stable decision making, adequate for use in a real commercial game project.
{"title":"Believable self-learning AI for world of tennis","authors":"M. Mozgovoy, Marina Purgina, I. Umarov","doi":"10.1109/CIG.2016.7860420","DOIUrl":"https://doi.org/10.1109/CIG.2016.7860420","url":null,"abstract":"We describe a method used to build a practical AI system for a mobile game of tennis. The chosen approach had to support two goals: (1) provide a large number of believable and diverse AI characters, and (2) let the users train AI “ghost” characters able to substitute them. We achieve these goals by learning AI agents from collected behavior data of human-controlled characters. The acquired knowledge is used by a case-based reasoning algorithm to perform human-like decision making. Our experiments show that the resulting agents indeed exhibit a variety of recognizable play styles, resembling the play styles of their human trainers. The resulting AI system demonstrated stable decision making, adequate for use in a real commercial game project.","PeriodicalId":6594,"journal":{"name":"2016 IEEE Conference on Computational Intelligence and Games (CIG)","volume":"49 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83074839","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 : 2016-09-01DOI: 10.1109/CIG.2016.7860393
Markus Viljanen, A. Airola, T. Pahikkala, J. Heikkonen
User activity in five mobile games is found to be accurately described by stochastic processes related to recurrent event models in survival analysis. We specify four simple parametric models and methods to fit them to data which specify this process within day accuracy in the individual user level. This model implies commonly used population level retention metrics: retention, rolling retention and lifetime retention. Furthermore, modelling aids in understanding the underlying phenomena generating these metrics, which is verified visually in five diverse mobile games. The model assists in obtaining analytical insight into frequency and longevity of product use and precipitates predictive modelling by forecasting their evolvement over time.
{"title":"Modelling user retention in mobile games","authors":"Markus Viljanen, A. Airola, T. Pahikkala, J. Heikkonen","doi":"10.1109/CIG.2016.7860393","DOIUrl":"https://doi.org/10.1109/CIG.2016.7860393","url":null,"abstract":"User activity in five mobile games is found to be accurately described by stochastic processes related to recurrent event models in survival analysis. We specify four simple parametric models and methods to fit them to data which specify this process within day accuracy in the individual user level. This model implies commonly used population level retention metrics: retention, rolling retention and lifetime retention. Furthermore, modelling aids in understanding the underlying phenomena generating these metrics, which is verified visually in five diverse mobile games. The model assists in obtaining analytical insight into frequency and longevity of product use and precipitates predictive modelling by forecasting their evolvement over time.","PeriodicalId":6594,"journal":{"name":"2016 IEEE Conference on Computational Intelligence and Games (CIG)","volume":"1 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88638541","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 : 2016-09-01DOI: 10.1109/CIG.2016.7860408
Daniele Gravina, Antonios Liapis, Georgios N. Yannakakis
In procedural content generation, it is often desirable to create artifacts which not only fulfill certain playability constraints but are also able to surprise the player with unexpected potential uses. This paper applies a divergent evolutionary search method based on surprise to the constrained problem of generating balanced and efficient sets of weapons for the Unreal Tournament III shooter game. The proposed constrained surprise search algorithm ensures that pairs of weapons are sufficiently balanced and effective while also rewarding unexpected uses of these weapons during game simulations with artificial agents. Results in the paper demonstrate that searching for surprise can create functionally diverse weapons which require new gameplay patterns of weapon use in the game.
{"title":"Constrained surprise search for content generation","authors":"Daniele Gravina, Antonios Liapis, Georgios N. Yannakakis","doi":"10.1109/CIG.2016.7860408","DOIUrl":"https://doi.org/10.1109/CIG.2016.7860408","url":null,"abstract":"In procedural content generation, it is often desirable to create artifacts which not only fulfill certain playability constraints but are also able to surprise the player with unexpected potential uses. This paper applies a divergent evolutionary search method based on surprise to the constrained problem of generating balanced and efficient sets of weapons for the Unreal Tournament III shooter game. The proposed constrained surprise search algorithm ensures that pairs of weapons are sufficiently balanced and effective while also rewarding unexpected uses of these weapons during game simulations with artificial agents. Results in the paper demonstrate that searching for surprise can create functionally diverse weapons which require new gameplay patterns of weapon use in the game.","PeriodicalId":6594,"journal":{"name":"2016 IEEE Conference on Computational Intelligence and Games (CIG)","volume":"15 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74561311","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 : 2016-09-01DOI: 10.1109/cig.2016.7860397
Peizhi Shi, Ke Chen
In procedural content generation (PCG), how to assure the quality of procedural games and how to provide effective control for designers are two major challenges. To tackle these issues, this paper exploits the synergy between rule-based and learning-based methods to produce quality yet controllable game segments in Super Mario Bros (SMB), hereinafter named constructive primitives (CPs). Easy-to-design rules are employed for removal of apparently unappealing game segments, and subsequent data-driven quality evaluation function is learned based on designer's annotations to deal with more complicated quality issues. The learned CPs provide not only quality game segments but also an effective control manner at a local level for designers. As a result, a complete quality game level can be generated online by integrating relevant constructive primitives via controllable parameters. Extensive simulation results demonstrate that the proposed approach efficiently generates controllable yet quality game levels in terms of different quality measures.
{"title":"Online level generation in Super Mario Bros via learning constructive primitives","authors":"Peizhi Shi, Ke Chen","doi":"10.1109/cig.2016.7860397","DOIUrl":"https://doi.org/10.1109/cig.2016.7860397","url":null,"abstract":"In procedural content generation (PCG), how to assure the quality of procedural games and how to provide effective control for designers are two major challenges. To tackle these issues, this paper exploits the synergy between rule-based and learning-based methods to produce quality yet controllable game segments in Super Mario Bros (SMB), hereinafter named constructive primitives (CPs). Easy-to-design rules are employed for removal of apparently unappealing game segments, and subsequent data-driven quality evaluation function is learned based on designer's annotations to deal with more complicated quality issues. The learned CPs provide not only quality game segments but also an effective control manner at a local level for designers. As a result, a complete quality game level can be generated online by integrating relevant constructive primitives via controllable parameters. Extensive simulation results demonstrate that the proposed approach efficiently generates controllable yet quality game levels in terms of different quality measures.","PeriodicalId":6594,"journal":{"name":"2016 IEEE Conference on Computational Intelligence and Games (CIG)","volume":"33 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79877389","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 : 2016-09-01DOI: 10.1109/CIG.2016.7860427
Naoyuki Sato, Kokolo Ikeda
Turn-based strategy games are interesting testbeds for developing artificial players because their rules present developers with several challenges. Currently, Monte-Carlo tree search variants are often utilized to address these challenges. However, we consider it worthwhile introducing minimax search variants with pruning techniques because a turn-based strategy is in some points similar to the games of chess and Shogi, in which minimax variants are known to be effective. Thus, we introduced three forward-pruning techniques to enable us to apply alpha beta search (as a minimax search variant) to turn-based strategy games. This type of search involves fixing unit action orders, generating unit actions selectively, and limiting the number of moving units in a search. We applied our proposed pruning methods by implementing an alpha beta-based artificial player in the Turn-based strategy Academic Package (TUBSTAP) open platform of our institute. This player competed against first- and second-rank players in the TUBSTAP AI competition in 2016. Our proposed player won against the other players in five different maps with an average winning ratio exceeding 70%.
{"title":"Three types of forward pruning techniques to apply the alpha beta algorithm to turn-based strategy games","authors":"Naoyuki Sato, Kokolo Ikeda","doi":"10.1109/CIG.2016.7860427","DOIUrl":"https://doi.org/10.1109/CIG.2016.7860427","url":null,"abstract":"Turn-based strategy games are interesting testbeds for developing artificial players because their rules present developers with several challenges. Currently, Monte-Carlo tree search variants are often utilized to address these challenges. However, we consider it worthwhile introducing minimax search variants with pruning techniques because a turn-based strategy is in some points similar to the games of chess and Shogi, in which minimax variants are known to be effective. Thus, we introduced three forward-pruning techniques to enable us to apply alpha beta search (as a minimax search variant) to turn-based strategy games. This type of search involves fixing unit action orders, generating unit actions selectively, and limiting the number of moving units in a search. We applied our proposed pruning methods by implementing an alpha beta-based artificial player in the Turn-based strategy Academic Package (TUBSTAP) open platform of our institute. This player competed against first- and second-rank players in the TUBSTAP AI competition in 2016. Our proposed player won against the other players in five different maps with an average winning ratio exceeding 70%.","PeriodicalId":6594,"journal":{"name":"2016 IEEE Conference on Computational Intelligence and Games (CIG)","volume":"115 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91129640","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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