2016 IEEE Conference on Computational Intelligence and Games (CIG)最新文献_第3页

Automated game balancing of asymmetric video games 非对称电子游戏的自动游戏平衡

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/CIG.2016.7860432

Philipp Beau, S. Bakkes

{"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}

引用次数: 15

Evolving missions to create game spaces 进化任务创造游戏空间

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/CIG.2016.7860396

Daniel Karavolos, Antonios Liapis, Georgios N. Yannakakis

引用次数: 12

Changing video game graphic styles using neural algorithms 使用神经算法改变电子游戏的图像风格

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/CIG.2016.7860390

Byung-Hak Yoo, Kyung-Joong Kim

引用次数: 3

A multi-objective genetic algorithm for simulating optimal fights in StarCraft II 用于模拟星际争霸2最优战斗的多目标遗传算法

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/CIG.2016.7860422

J. Schmitt, H. Köstler

{"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}

引用次数: 8

I am a legend: Hacking hearthstone using statistical learning methods 我是一个传奇:使用统计学习方法破解炉石传说

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/CIG.2016.7860416

Elie Bursztein

引用次数: 30

Believable self-learning AI for world of tennis 可信的自我学习AI网球世界

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/CIG.2016.7860420

M. Mozgovoy, Marina Purgina, I. Umarov

引用次数: 7

Modelling user retention in mobile games 模拟手机游戏的用户留存率

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/CIG.2016.7860393

Markus Viljanen, A. Airola, T. Pahikkala, J. Heikkonen

引用次数: 16

Constrained surprise search for content generation 内容生成的约束惊喜搜索

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/CIG.2016.7860408

Daniele Gravina, Antonios Liapis, Georgios N. Yannakakis

引用次数: 21

Online level generation in Super Mario Bros via learning constructive primitives 在《超级马里奥兄弟》中通过学习建设性原语生成在线关卡

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/cig.2016.7860397

Peizhi Shi, Ke Chen

引用次数: 11

Three types of forward pruning techniques to apply the alpha beta algorithm to turn-based strategy games 将alpha - beta算法应用于回合制策略游戏的三种前向修剪技术

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/CIG.2016.7860427

Naoyuki Sato, Kokolo Ikeda

{"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}

引用次数: 5