2015 IEEE Conference on Computational Intelligence and Games (CIG)最新文献_第2页

StarCraft: Brood War — Strategy powered by the SOMA swarm algorithm 星际争霸:母巢之战-由SOMA群算法驱动的策略

2015 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2015-11-05 DOI: 10.1109/CIG.2015.7317903

I. Zelinka, Lubomir Sikora

引用次数: 16

Designing bots in games with a purpose 在游戏中设计带有目的性的机器人

2015 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2015-11-05 DOI: 10.1109/CIG.2015.7317947

G. Baroffio, Luca Galli, P. Fraternali

{"title":"Designing bots in games with a purpose","authors":"G. Baroffio, Luca Galli, P. Fraternali","doi":"10.1109/CIG.2015.7317947","DOIUrl":"https://doi.org/10.1109/CIG.2015.7317947","url":null,"abstract":"The massive amount of time that people spend in online gaming is being increasingly exploited by a particular kind of Serious Games, the Games with a Purpose (GWAP), used to solve complex problems as a byproduct of a collaborative gameplay. The required tasks are solved by exploiting game mechanics that often require the submission of thousands of players' annotations, to achieve a robust estimate of the results. Gathering a consistent playerbase able to solve computational problems at a scale is extremely difficult, due to better entertainment alternatives on the market and the necessity of pairing each player with another one due to the inherent multiplayer nature of this genre. Artificial players (bots) may be introduced when the online platform has not enough human contributors to employ, but their functional requirements and implementation is much different than the one of traditional videogames. In this work we describe the framework and the design choices that have been used to implement a bot in an existing GWAP for fashion garment image segmentation, showing how supervised methods can be applied effectively to emulate human behaviour in the resolution of computational tasks through gameplay actions.","PeriodicalId":244862,"journal":{"name":"2015 IEEE Conference on Computational Intelligence and Games (CIG)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127762050","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: 0

Towards automatic StarCraft strategy generation using genetic programming 利用遗传编程实现星际争霸策略的自动生成

2015 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2015-11-05 DOI: 10.1109/CIG.2015.7317940

P. García-Sánchez, A. Tonda, A. García, Giovanni Squillero, J. J. M. Guervós

{"title":"Towards automatic StarCraft strategy generation using genetic programming","authors":"P. García-Sánchez, A. Tonda, A. García, Giovanni Squillero, J. J. M. Guervós","doi":"10.1109/CIG.2015.7317940","DOIUrl":"https://doi.org/10.1109/CIG.2015.7317940","url":null,"abstract":"Among Real-Time Strategy games few titles have enjoyed the continued success of StarCraft. Many research lines aimed at developing Artificial Intelligences, or “bots”, capable of challenging human players, use StarCraft as a platform. Several characteristics make this game particularly appealing for researchers, such as: asymmetric balanced factions, considerable complexity of the technology trees, large number of units with unique features, and potential for optimization both at the strategical and tactical level. In literature, various works exploit evolutionary computation to optimize particular aspects of the game, from squad formation to map exploration; but so far, no evolutionary approach has been applied to the development of a complete strategy from scratch. In this paper, we present the preliminary results of StarCraftGP, a framework able to evolve a complete strategy for StarCraft, from the building plan, to the composition of squads, up to the set of rules that define the bot's behavior during the game. The proposed approach generates strategies as C++ classes, that are then compiled and executed inside the OpprimoBot open-source framework. In a first set of runs, we demonstrate that StarCraftGP ultimately generates a competitive strategy for a Zerg bot, able to defeat several human-designed bots.","PeriodicalId":244862,"journal":{"name":"2015 IEEE Conference on Computational Intelligence and Games (CIG)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116609133","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: 30

Mobile EEG & ECG integration system for monitoring physiological states in peforming simulated war game training 用于模拟战争训练中生理状态监测的移动脑电心电集成系统

2015 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2015-11-05 DOI: 10.1109/CIG.2015.7317900

L. Ko, Peng-Wen Lai, Bao-Jun Yang, Chin-Teng Lin

引用次数: 6

Estimation of player's preference for cooperative RPGs using multi-strategy Monte-Carlo method 基于多策略蒙特卡罗方法的合作rpg玩家偏好估计

2015 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2015-11-05 DOI: 10.1109/CIG.2015.7317935

Naoyuki Sato, Kokolo Ikeda, T. Wada

{"title":"Estimation of player's preference for cooperative RPGs using multi-strategy Monte-Carlo method","authors":"Naoyuki Sato, Kokolo Ikeda, T. Wada","doi":"10.1109/CIG.2015.7317935","DOIUrl":"https://doi.org/10.1109/CIG.2015.7317935","url":null,"abstract":"In many video games such as role playing games (RPGs) or sports games, computer players act not only as the opponents of the human player but also as team-mates. But computer players as team-mates often behave in a way that human players do not expect, and such mismatches cause bigger dissatisfaction than in the case of computer players as opponents., One of the reasons for such mismatches is that there are several types of sub-goals or play-styles in these games and the AI players act without understanding the human player's preference about them. The purpose of this study is to propose a method for developing computer team-mate players that estimate the sub-goal preferences of the team-mate human player and act according to these preferences., For this purpose, we modeled the preferences of sub-goals as a function and decided the most likely parameters by a multi-strategy Monte-Carlo method, by referring to the past actions selected by the team-mate human player., Additionally, we evaluated the proposed method through two series of experiments, one by using artificial players with various sub-goal preferences and another one by using human players. The experiments showed that the proposed method can estimate their preferences after a few games, and can decrease the dissatisfaction of human players.","PeriodicalId":244862,"journal":{"name":"2015 IEEE Conference on Computational Intelligence and Games (CIG)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133189239","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: 3

Co-evolution of strategies for multi-objective games under postponed objective preferences 延迟目标偏好下多目标博弈策略的协同进化

2015 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2015-11-05 DOI: 10.1109/CIG.2015.7317915

Erella Eisenstadt, A. Moshaiov, G. Avigad

{"title":"Co-evolution of strategies for multi-objective games under postponed objective preferences","authors":"Erella Eisenstadt, A. Moshaiov, G. Avigad","doi":"10.1109/CIG.2015.7317915","DOIUrl":"https://doi.org/10.1109/CIG.2015.7317915","url":null,"abstract":"The vast majority of studies that are related to game theory are on Single Objective Games (SOG), also known as single payoff games. Multi-Objective Games (MOGs), which are also termed as multi payoff, multi criteria or vector payoff games, have received lesser attention. Yet, in many practical problems, generally each player cope with multiple objectives that might be contradicting. In such problems, a vector of objective functions must be considered. The common approach to deal with MOGs is to assume that the preferences of the players are known. In such a case a utility function is used, which transforms the MOG into a surrogate SOG., This paper deals with non-cooperative MOGs in a non-traditional way. The zero-sum MOG, which is considered here, involves two players that postponed their objective preferences, allowing them to decide on their preferences after tradeoffs are revealed. To solve such problems we propose a co-evolutionary algorithm based on a worst-case domination relation among sets. The suggested algorithm is tested on a simple differential game (tug-of-war). The obtained results serve to illustrate the approach and demonstrate the applicability of the proposed co-evolutionary algorithm.","PeriodicalId":244862,"journal":{"name":"2015 IEEE Conference on Computational Intelligence and Games (CIG)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133070713","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: 12

Building a computer Mahjong player based on Monte Carlo simulation and opponent models 建立了一个基于蒙特卡罗仿真和对手模型的计算机麻将播放器

2015 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2015-11-05 DOI: 10.1109/CIG.2015.7317929

Naoki Mizukami, Yoshimasa Tsuruoka

引用次数: 31

MCTS with influence map for general video game playing MCTS与影响地图一般视频游戏玩

2015 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2015-11-05 DOI: 10.1109/CIG.2015.7317896

Hyun-Soo Park, Kyung-Joong Kim

引用次数: 16

Evaluating team behaviors constructed with human-guided machine learning 评估由人类引导的机器学习构建的团队行为

2015 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2015-11-05 DOI: 10.1109/CIG.2015.7317946

Igor Karpov, Leif M. Johnson, R. Miikkulainen

引用次数: 5

Perception simulation in social planning for emergent storytelling 突发故事叙事社会规划中的感知模拟

2015 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2015-11-05 DOI: 10.1109/CIG.2015.7317922

D. Carvalho, E. Clua, A. Paes

{"title":"Perception simulation in social planning for emergent storytelling","authors":"D. Carvalho, E. Clua, A. Paes","doi":"10.1109/CIG.2015.7317922","DOIUrl":"https://doi.org/10.1109/CIG.2015.7317922","url":null,"abstract":"The immersion level of a game is one of the main factors that affect its quality. The higher the immersion, the more connected to the game the player feels. To increase immersion, a number of games try to adapt the story to at least create the illusion that the player's actions and decisions are guiding it. However, this adaptation is often limited, since creating story variations to each of the player's actions would be infeasible. One possible solution, which has been studied in the storytelling area, is to allow the game itself to generate its story as it is being played. One of the main methods for generating stories is based on simulating virtual worlds inhabited with agents to impersonate their characters. Although stories frequently rely on their characters' misunderstandings and knowledge failures to develop interesting situations, most storytelling approaches available in the literature are based on correct and perfect reasoning. As such, they are less likely to make scenarios based on mistakes to emerge. In this paper we enhance a perception simulation method, that allows characters to make wrong but coherent choices, with a reasoning process that deals with uncertainty and the knowledge of the others. Our main goal is to develop story generation capacity in simulation based systems. As test scenario we recreated Little Red Riding Hood story world, with which our method generated coherent variations of the story, where characters made decisions based on perception without recurring to predefined scripts.","PeriodicalId":244862,"journal":{"name":"2015 IEEE Conference on Computational Intelligence and Games (CIG)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115329410","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