Proceedings of the 17th International Conference on the Foundations of Digital Games最新文献_第7页

A Gap in Games Research: Reflecting on Two Camps and a Bridge 游戏研究中的鸿沟:关于两个阵营和桥梁的思考

Proceedings of the 17th International Conference on the Foundations of Digital Games Pub Date : 2022-09-05 DOI: 10.1145/3555858.3555916

Henrik Warpefelt

引用次数: 0

Blink To Win: Blink Patterns of Video Game Players Are Connected to Expertise 眨眼取胜:电子游戏玩家的眨眼模式与专业技能有关

Proceedings of the 17th International Conference on the Foundations of Digital Games Pub Date : 2022-09-05 DOI: 10.1145/3555858.3555864

Gianluca Guglielmo, Paris Mavromoustakos Blom, M. Klincewicz, E. M. J. Huis In 't Veld, P. Spronck

引用次数: 2

Towards Understanding Player Behavior in Blockchain Games: A Case Study of Aavegotchi 如何理解区块链游戏中的玩家行为:以《avegotchi》为例

Proceedings of the 17th International Conference on the Foundations of Digital Games Pub Date : 2022-09-05 DOI: 10.1145/3555858.3555883

Yu Jiang, Tian Min, Sizheng Fan, Rongqi Tao, Wei Cai

{"title":"Towards Understanding Player Behavior in Blockchain Games: A Case Study of Aavegotchi","authors":"Yu Jiang, Tian Min, Sizheng Fan, Rongqi Tao, Wei Cai","doi":"10.1145/3555858.3555883","DOIUrl":"https://doi.org/10.1145/3555858.3555883","url":null,"abstract":"Blockchain games introduce unique gameplay and incentive mechanisms by allowing players to be rewarded with in-game assets or tokens through financial activities. However, most blockchain games are not comparable to traditional games in terms of lifespan and player engagement. In this paper, we try to see the big picture in a small way to explore and determine the impact of gameplay and financial factors on player behavior in blockchain games. Taking Aavegotchi as an example, we collect one year of operation data to build player profiles. We perform an in-depth analysis of player behavior from the macroscopic data and apply an unsupervised clustering method to distinguish the attraction of the gameplay and incentives. Our results reveal that the whole game is held up by a small number of players with high-frequent interaction or vast amounts of funds invested. Financial incentives are indispensable for blockchain games for they provide attraction and optional ways for players to engage with the game. However, financial services are tightly linked to the free market. The game will face an irreversible loss of players when the market experiences depression. For blockchain games, well-designed gameplay should be the fundamental basis for the long-lasting retention of players.","PeriodicalId":290159,"journal":{"name":"Proceedings of the 17th International Conference on the Foundations of Digital Games","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133079814","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}

引用次数: 7

SketchBetween: Video-to-Video Synthesis for Sprite Animation via Sketches SketchBetween:视频到视频合成精灵动画通过草图

Proceedings of the 17th International Conference on the Foundations of Digital Games Pub Date : 2022-09-01 DOI: 10.1145/3555858.3555928

Dagmar Lukka Loftsd'ottir, Matthew J. Guzdial

引用次数: 0

EvolvingBehavior: Towards Co-Creative Evolution of Behavior Trees for Game NPCs EvolvingBehavior:游戏npc行为树的共同创新进化

Proceedings of the 17th International Conference on the Foundations of Digital Games Pub Date : 2022-09-01 DOI: 10.1145/3555858.3555896

Nathan Partlan, Luis Soto, J. Howe, Sarthak Shrivastava, M. S. El-Nasr, S. Marsella

引用次数: 1

Generative Personas That Behave and Experience Like Humans 像人类一样行为和经历的生成式人物角色

Proceedings of the 17th International Conference on the Foundations of Digital Games Pub Date : 2022-08-26 DOI: 10.1145/3555858.3555879

M. Barthet, A. Khalifa, Antonios Liapis, Georgios N. Yannakakis

{"title":"Generative Personas That Behave and Experience Like Humans","authors":"M. Barthet, A. Khalifa, Antonios Liapis, Georgios N. Yannakakis","doi":"10.1145/3555858.3555879","DOIUrl":"https://doi.org/10.1145/3555858.3555879","url":null,"abstract":"Using artificial intelligence (AI) to automatically test a game remains a critical challenge for the development of richer and more complex game worlds and for the advancement of AI at large. One of the most promising methods for achieving that long-standing goal is the use of generative AI agents, namely procedural personas, that attempt to imitate particular playing behaviors which are represented as rules, rewards, or human demonstrations. All research efforts for building those generative agents, however, have focused solely on playing behavior which is arguably a narrow perspective of what a player actually does in a game. Motivated by this gap in the existing state of the art, in this paper we extend the notion of behavioral procedural personas to cater for player experience, thus examining generative agents that can both behave and experience their game as humans would. For that purpose, we employ the Go-Explore reinforcement learning paradigm for training human-like procedural personas, and we test our method on behavior and experience demonstrations of more than 100 players of a racing game. Our findings suggest that the generated agents exhibit distinctive play styles and experience responses of the human personas they were designed to imitate. Importantly, it also appears that experience, which is tied to playing behavior, can be a highly informative driver for better behavioral exploration.","PeriodicalId":290159,"journal":{"name":"Proceedings of the 17th International Conference on the Foundations of Digital Games","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127993638","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}

引用次数: 11

Game State Learning via Game Scene Augmentation 通过游戏场景增强学习游戏状态

Proceedings of the 17th International Conference on the Foundations of Digital Games Pub Date : 2022-07-04 DOI: 10.1145/3555858.3555902

C. Trivedi, Konstantinos Makantasis, Antonios Liapis, Georgios N. Yannakakis

{"title":"Game State Learning via Game Scene Augmentation","authors":"C. Trivedi, Konstantinos Makantasis, Antonios Liapis, Georgios N. Yannakakis","doi":"10.1145/3555858.3555902","DOIUrl":"https://doi.org/10.1145/3555858.3555902","url":null,"abstract":"Having access to accurate game state information is of utmost importance for any artificial intelligence task including game-playing, testing, player modeling, and procedural content generation. Self-Supervised Learning (SSL) techniques have shown to be capable of inferring accurate game state information from the high-dimensional pixel input of game footage into compressed latent representations. Contrastive Learning is a popular SSL paradigm where the visual understanding of the game’s images comes from contrasting dissimilar and similar game states defined by simple image augmentation methods. In this study, we introduce a new game scene augmentation technique—named GameCLR—that takes advantage of the game-engine to define and synthesize specific, highly-controlled renderings of different game states, thereby, boosting contrastive learning performance. We test our GameCLR technique on images of the CARLA driving simulator environment and compare it against the popular SimCLR baseline SSL method. Our results suggest that GameCLR can infer the game’s state information from game footage more accurately compared to the baseline. Our proposed approach allows us to conduct game artificial intelligence research by directly utilizing screen pixels as input.","PeriodicalId":290159,"journal":{"name":"Proceedings of the 17th International Conference on the Foundations of Digital Games","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116497618","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}

引用次数: 1

Learning Controllable 3D Level Generators 学习可控的3D关卡生成器

Proceedings of the 17th International Conference on the Foundations of Digital Games Pub Date : 2022-06-27 DOI: 10.1145/3555858.3563273

Zehua Jiang, Sam Earle, M. Green, J. Togelius

{"title":"Learning Controllable 3D Level Generators","authors":"Zehua Jiang, Sam Earle, M. Green, J. Togelius","doi":"10.1145/3555858.3563273","DOIUrl":"https://doi.org/10.1145/3555858.3563273","url":null,"abstract":"Procedural Content Generation via Reinforcement Learning (PCGRL) foregoes the need for large human-authored data-sets and allows agents to train explicitly on functional constraints, using computable, user-defined measures of quality instead of target output. We explore the application of PCGRL to 3D domains, in which content-generation tasks naturally have greater complexity and potential pertinence to real-world applications. Here, we introduce several PCGRL tasks for the 3D domain, Minecraft. These tasks will challenge RL-based generators using affordances often found in 3D environments, such as jumping, multiple dimensional movement, and gravity. We train agents to optimize each of these tasks to explore the capabilities of existing in PCGRL. The agents are able to generate relatively complex and diverse levels, and generalize to random initial states and control targets. Controllability tests in the presented tasks demonstrate their utility to analyze success and failure for 3D generators. We argue that these generators could serve both as co-creative tools for game designers, and as pre-trained environment generators in curriculum learning for player agents.","PeriodicalId":290159,"journal":{"name":"Proceedings of the 17th International Conference on the Foundations of Digital Games","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132004888","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}

引用次数: 6

Mutation Models: Learning to Generate Levels by Imitating Evolution 突变模型:通过模仿进化来学习生成关卡

Proceedings of the 17th International Conference on the Foundations of Digital Games Pub Date : 2022-06-11 DOI: 10.1145/3555858.3563267

A. Khalifa, J. Togelius, M. Green

{"title":"Mutation Models: Learning to Generate Levels by Imitating Evolution","authors":"A. Khalifa, J. Togelius, M. Green","doi":"10.1145/3555858.3563267","DOIUrl":"https://doi.org/10.1145/3555858.3563267","url":null,"abstract":"Search-based procedural content generation (PCG) is a well-known method for level generation in games. Its key advantage is that it is generic and able to satisfy functional constraints. However, due to the heavy computational costs to run these algorithms online, search-based PCG is rarely utilized for real-time generation. In this paper, we introduce mutation models, a new type of iterative level generator based on machine learning. We train a model to imitate the evolutionary process and use the trained model to generate levels. This trained model is able to modify noisy levels sequentially to create better levels without the need for a fitness function during inference. We evaluate our trained models on a 2D maze generation task. We compare several different versions of the method: training the models either at the end of evolution (normal evolution) or every 100 generations (assisted evolution) and using the model as a mutation function during evolution. Using the assisted evolution process, the final trained models are able to generate mazes with a success rate of and high diversity of . The trained model is many times faster than the evolutionary process it was trained on. This work opens the door to a new way of learning level generators guided by an evolutionary process, meaning automatic creation of generators with specifiable constraints and objectives that are fast enough for runtime deployment in games.","PeriodicalId":290159,"journal":{"name":"Proceedings of the 17th International Conference on the Foundations of Digital Games","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129795576","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}

引用次数: 5

The Dark Souls of Archaeology: Recording Elden Ring 考古学的黑暗之魂:记录黄金戒指

Proceedings of the 17th International Conference on the Foundations of Digital Games Pub Date : 2022-04-22 DOI: 10.1145/3555858.3555889

Florence Smith Nicholls, Michael Cook

引用次数: 2