George E.M. Long;Diego Perez-Liebana;Spyridon Samothrakis
In miniature wargames, such as Warhammer 40k , players control asymmetrical armies, which include multiple units of different types and strengths. These games often use point costs to balance the armies. Each unit is assigned a point cost, and players have a budget they can spend on units. Calculating accurate point costs can be a tedious manual process, with iterative playtests required. If these point costs do not represent a units true power, the game can get unbalanced as overpowered units can have low point costs. In our previous paper, we proposed an automated way of estimating the point costs using a linear regression approach. We used a turn-based asymmetrical wargame called Wizard Wars to test our methods. Players were simulated using Monte Carlo tree search, using different heuristics to represent playstyles. We presented six variants of our method, and show that one method was able to reduce the unbalanced nature of the game by almost half. For this article, we introduce a framework called simple testing and evaluation of points, which allows for further and more granular analysis of point cost estimating methods, by providing a fast, simple, and configurable framework to test methods with. Finally, we compare how our methods do in Wizard Wars against expertly chosen point costs.
{"title":"STEP: A Framework for Automated Point Cost Estimation","authors":"George E.M. Long;Diego Perez-Liebana;Spyridon Samothrakis","doi":"10.1109/TG.2024.3450992","DOIUrl":"10.1109/TG.2024.3450992","url":null,"abstract":"In miniature wargames, such as \u0000<italic>Warhammer 40k</i>\u0000, players control asymmetrical armies, which include multiple units of different types and strengths. These games often use point costs to balance the armies. Each unit is assigned a point cost, and players have a budget they can spend on units. Calculating accurate point costs can be a tedious manual process, with iterative playtests required. If these point costs do not represent a units true power, the game can get unbalanced as overpowered units can have low point costs. In our previous paper, we proposed an automated way of estimating the point costs using a linear regression approach. We used a turn-based asymmetrical wargame called \u0000<italic>Wizard Wars</i>\u0000 to test our methods. Players were simulated using Monte Carlo tree search, using different heuristics to represent playstyles. We presented six variants of our method, and show that one method was able to reduce the unbalanced nature of the game by almost half. For this article, we introduce a framework called simple testing and evaluation of points, which allows for further and more granular analysis of point cost estimating methods, by providing a fast, simple, and configurable framework to test methods with. Finally, we compare how our methods do in \u0000<italic>Wizard Wars</i>\u0000 against expertly chosen point costs.","PeriodicalId":55977,"journal":{"name":"IEEE Transactions on Games","volume":"16 4","pages":"927-936"},"PeriodicalIF":1.7,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Monica Villanueva Aylagas;Joakim Bergdahl;Jonas Gillberg;Alessandro Sestini;Theodor Tolstoy;Linus Gisslén
Generative models for level generation have shown great potential in game production. However, they often provide limited control over the generation, and the validity of the generated levels is unreliable. Despite this fact, only a few approaches that learn from existing data provide the users with ways of controlling the generation, simultaneously addressing the generation of unsolvable levels. This article proposes autovalidated level generation, a novel method to improve models that learn from existing level designs using difficulty statistics extracted from gameplay. In particular, we use a conditional variational autoencoder to generate layouts for match-3 levels, conditioning the model on precollected statistics, such as game mechanics like difficulty, and relevant visual features, such as size and symmetry. Our method is general enough that multiple approaches could potentially be used to generate these statistics. We quantitatively evaluate our approach by comparing it to an ablated model without difficulty conditioning. In addition, we analyze both quantitatively and qualitatively whether the style of the dataset is preserved in the generated levels. Our approach generates more valid levels than the same method without difficulty conditioning.
{"title":"Improving Conditional Level Generation Using Automated Validation in Match-3 Games","authors":"Monica Villanueva Aylagas;Joakim Bergdahl;Jonas Gillberg;Alessandro Sestini;Theodor Tolstoy;Linus Gisslén","doi":"10.1109/TG.2024.3440214","DOIUrl":"10.1109/TG.2024.3440214","url":null,"abstract":"Generative models for level generation have shown great potential in game production. However, they often provide limited control over the generation, and the validity of the generated levels is unreliable. Despite this fact, only a few approaches that learn from existing data provide the users with ways of controlling the generation, simultaneously addressing the generation of unsolvable levels. This article proposes autovalidated level generation, a novel method to improve models that learn from existing level designs using difficulty statistics extracted from gameplay. In particular, we use a conditional variational autoencoder to generate layouts for match-3 levels, conditioning the model on precollected statistics, such as game mechanics like difficulty, and relevant visual features, such as size and symmetry. Our method is general enough that multiple approaches could potentially be used to generate these statistics. We quantitatively evaluate our approach by comparing it to an ablated model without difficulty conditioning. In addition, we analyze both quantitatively and qualitatively whether the style of the dataset is preserved in the generated levels. Our approach generates more valid levels than the same method without difficulty conditioning.","PeriodicalId":55977,"journal":{"name":"IEEE Transactions on Games","volume":"16 4","pages":"783-792"},"PeriodicalIF":1.7,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141944749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Biosignal Contrastive Representation Learning for Emotion Recognition of Game Users","authors":"Rongyang Li, Jianguo Ding, Huansheng Ning","doi":"10.1109/tg.2024.3435339","DOIUrl":"https://doi.org/10.1109/tg.2024.3435339","url":null,"abstract":"","PeriodicalId":55977,"journal":{"name":"IEEE Transactions on Games","volume":"21 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141863122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Detecting Discrepancies between Subtitles and Audio in Gameplay Videos with EchoTest","authors":"Ian Gauk, Cor-Paul Bezemer","doi":"10.1109/tg.2024.3435799","DOIUrl":"https://doi.org/10.1109/tg.2024.3435799","url":null,"abstract":"","PeriodicalId":55977,"journal":{"name":"IEEE Transactions on Games","volume":"44 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141863123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Modeling human players' behaviors in games is a key challenge for making natural computer players, evaluating games, and generating content. To achieve better human–computer interaction, researchers have tried various methods to create human-like artificial intelligence. In chess and Go , supervised learning with deep neural networks is known as one of the most effective ways to predict human moves. However, for many other games (e.g., Shogi ), it is hard to collect a similar amount of game records, resulting in poor move-matching accuracy of the supervised learning. We propose a method to compensate for the weakness of the supervised learning policy by Blending it with an AlphaZero-like reinforcement learning policy. Experiments on Shogi showed that the Blend method significantly improved the move-matching accuracy over supervised learning models. Experiments on chess and Go with a limited number of game records also showed similar results. The Blend method was effective with both medium and large numbers of games, particularly the medium case. We confirmed the robustness of the Blend model to the parameter and discussed the mechanism why the move-matching accuracy improves. In addition, we showed that the Blend model performed better than existing work that tried to improve the move-matching accuracy.
{"title":"More Human-Like Gameplay by Blending Policies From Supervised and Reinforcement Learning","authors":"Tatsuyoshi Ogawa;Chu-Hsuan Hsueh;Kokolo Ikeda","doi":"10.1109/TG.2024.3424668","DOIUrl":"10.1109/TG.2024.3424668","url":null,"abstract":"Modeling human players' behaviors in games is a key challenge for making natural computer players, evaluating games, and generating content. To achieve better human–computer interaction, researchers have tried various methods to create human-like artificial intelligence. In chess and \u0000<italic>Go</i>\u0000, supervised learning with deep neural networks is known as one of the most effective ways to predict human moves. However, for many other games (e.g., \u0000<italic>Shogi</i>\u0000), it is hard to collect a similar amount of game records, resulting in poor move-matching accuracy of the supervised learning. We propose a method to compensate for the weakness of the supervised learning policy by Blending it with an AlphaZero-like reinforcement learning policy. Experiments on \u0000<italic>Shogi</i>\u0000 showed that the Blend method significantly improved the move-matching accuracy over supervised learning models. Experiments on chess and \u0000<italic>Go</i>\u0000 with a limited number of game records also showed similar results. The Blend method was effective with both medium and large numbers of games, particularly the medium case. We confirmed the robustness of the Blend model to the parameter and discussed the mechanism why the move-matching accuracy improves. In addition, we showed that the Blend model performed better than existing work that tried to improve the move-matching accuracy.","PeriodicalId":55977,"journal":{"name":"IEEE Transactions on Games","volume":"16 4","pages":"831-843"},"PeriodicalIF":1.7,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10595450","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141611394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pablo Gutiérrez-Sánchez;Marco A. Gómez-Martín;Pedro A. González-Calero;Pedro P. Gómez-Martín
In video games, the validation of design specifications throughout the development process poses a major challenge as the project grows in complexity and scale and purely manual testing becomes very costly. This article proposes a new approach to design validation regression testing based on a reinforcement learning technique guided by tasks expressed in a formal logic specification language (truncated linear temporal logic) and the progress made in completing these tasks. This requires no prior knowledge of machine learning to train testing bots, is naturally interpretable and debuggable, and produces dense reward functions without the need for reward shaping. We investigate the validity of our strategy by comparing it to an imitation baseline in experiments organized around three use cases of typical scenarios in commercial video games on a 3-D stealth testing environment created in unity. For each scenario, we analyze the agents' reactivity to modifications in common assets to accommodate design needs in other sections of the game, and their ability to report unexpected gameplay variations. Our experiments demonstrate the practicality of our approach for training bots to conduct automated regression testing in complex video game settings.
{"title":"A Progress-Based Algorithm for Interpretable Reinforcement Learning in Regression Testing","authors":"Pablo Gutiérrez-Sánchez;Marco A. Gómez-Martín;Pedro A. González-Calero;Pedro P. Gómez-Martín","doi":"10.1109/TG.2024.3426601","DOIUrl":"10.1109/TG.2024.3426601","url":null,"abstract":"In video games, the validation of design specifications throughout the development process poses a major challenge as the project grows in complexity and scale and purely manual testing becomes very costly. This article proposes a new approach to design validation regression testing based on a reinforcement learning technique guided by tasks expressed in a formal logic specification language (truncated linear temporal logic) and the progress made in completing these tasks. This requires no prior knowledge of machine learning to train testing bots, is naturally interpretable and debuggable, and produces dense reward functions without the need for reward shaping. We investigate the validity of our strategy by comparing it to an imitation baseline in experiments organized around three use cases of typical scenarios in commercial video games on a 3-D stealth testing environment created in unity. For each scenario, we analyze the agents' reactivity to modifications in common assets to accommodate design needs in other sections of the game, and their ability to report unexpected gameplay variations. Our experiments demonstrate the practicality of our approach for training bots to conduct automated regression testing in complex video game settings.","PeriodicalId":55977,"journal":{"name":"IEEE Transactions on Games","volume":"16 4","pages":"844-853"},"PeriodicalIF":1.7,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10595449","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141614866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In imperfect information games, the game state is generally not fully observable to players. Therefore, good gameplay requires policies that deal with the different information that is hidden from each player. To combat this, effective algorithms often reason about information sets; the sets of all possible game states that are consistent with a player's observations. While there is no way to distinguish between the states within an information set, this property does not imply that all states are equally likely to occur in play. We extend previous research on assigning weights to the states in an information set in order to facilitate better gameplay in the imperfect information game of reconnaissance blind chess (RBC). For this, we train two different neural networks, which estimate the likelihood of each state in an information set from historical game data. Experimentally, we find that a Siamese neural network is able to achieve higher accuracy and is more efficient than a classical convolutional neural network for the given domain. Finally, we evaluate an RBC-playing agent that is based on the generated weightings and compare different parameter settings that influence how strongly it should rely on them. The resulting best player is ranked 5th on the public leaderboard.
{"title":"Neural Network-Based Information Set Weighting for Playing Reconnaissance Blind Chess","authors":"Timo Bertram;Johannes Fürnkranz;Martin Müller","doi":"10.1109/TG.2024.3425803","DOIUrl":"10.1109/TG.2024.3425803","url":null,"abstract":"In imperfect information games, the game state is generally not fully observable to players. Therefore, good gameplay requires policies that deal with the different information that is hidden from each player. To combat this, effective algorithms often reason about information sets; the sets of all possible game states that are consistent with a player's observations. While there is no way to distinguish between the states within an information set, this property does not imply that all states are equally likely to occur in play. We extend previous research on assigning weights to the states in an information set in order to facilitate better gameplay in the imperfect information game of reconnaissance blind chess (RBC). For this, we train two different neural networks, which estimate the likelihood of each state in an information set from historical game data. Experimentally, we find that a Siamese neural network is able to achieve higher accuracy and is more efficient than a classical convolutional neural network for the given domain. Finally, we evaluate an RBC-playing agent that is based on the generated weightings and compare different parameter settings that influence how strongly it should rely on them. The resulting best player is ranked 5th on the public leaderboard.","PeriodicalId":55977,"journal":{"name":"IEEE Transactions on Games","volume":"16 4","pages":"960-970"},"PeriodicalIF":1.7,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10592629","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141588601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Christina Volioti, Vasileios Martsis, Apostolos Ampatzoglou, Euclid Keramopoulos, Alexander Chatzigeorgiou
{"title":"Codeless3D: Design and Usability Evaluation of a Low-Code Tool for 3D Game Generation","authors":"Christina Volioti, Vasileios Martsis, Apostolos Ampatzoglou, Euclid Keramopoulos, Alexander Chatzigeorgiou","doi":"10.1109/tg.2024.3424894","DOIUrl":"https://doi.org/10.1109/tg.2024.3424894","url":null,"abstract":"","PeriodicalId":55977,"journal":{"name":"IEEE Transactions on Games","volume":"38 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141574661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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