2015 IEEE Conference on Computational Intelligence and Games (CIG)最新文献_第4页

Flexible story generation with norms and preferences in computer role playing games 计算机角色扮演游戏中具有规范和偏好的灵活故事生成

2015 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2015-11-05 DOI: 10.1109/CIG.2015.7317953

Edward Booth, John Thangarajah, Fabio Zambetta

引用次数: 0

Combining pathfmding algorithm with Knowledge-based Monte-Carlo tree search in general video game playing 将路径生成算法与基于知识的蒙特卡罗树搜索相结合用于一般视频游戏

2015 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2015-11-05 DOI: 10.1109/CIG.2015.7317898

C. Chu, Hisaaki Hashizume, Zikun Guo, Tomohiro Harada, R. Thawonmas

引用次数: 9

Improving Monte-Carlo tree search for dots-and-boxes with a novel board representation and artificial neural networks 用一种新颖的棋盘表示和人工神经网络改进点盒的蒙特卡洛树搜索

2015 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2015-11-05 DOI: 10.1109/CIG.2015.7317912

Yimeng Zhuang, Shuqin Li, Tom Peters, Chenguang Zhang

{"title":"Improving Monte-Carlo tree search for dots-and-boxes with a novel board representation and artificial neural networks","authors":"Yimeng Zhuang, Shuqin Li, Tom Peters, Chenguang Zhang","doi":"10.1109/CIG.2015.7317912","DOIUrl":"https://doi.org/10.1109/CIG.2015.7317912","url":null,"abstract":"Dots-and-Boxes is a well-known paper-and-pencil, game for two players. It reaches a high level of complexity, posing an interesting challenge for AI development. Previous, board representation techniques for Dots-and-Boxes rely on data, structures like arrays or linked lists to facilitate operations on the, board. These representation techniques usually lack for the ability, to incrementally update information required for efficient move, generation during search. To address this problem a novel board, representation for Dots-and-Boxes is proposed in this paper. It, utilizes game-specific knowledge to classify distinct conditions on, the board and its implementation is based on disjoint-sets. Besides, the novel board representation this paper treats optimizations for, Monte-Carlo Tree Search (MCTS) focusing on artificial neural, networks. Finally we implemented our proposed approach in a new program called QDab and conducted experiments showing, that the new board representation improves the efficiency of basic, operations on the board by more than 6 times. Further tests, against other implementations show the superior playing strength, of our approach.","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":"122523614","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: 9

A reinforcement learning approach for the circle agent of geometry friends 几何朋友圈代理的一种强化学习方法

2015 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2015-11-05 DOI: 10.1109/CIG.2015.7317938

Joao Quiterio, R. Prada, Francisco S. Melo

{"title":"A reinforcement learning approach for the circle agent of geometry friends","authors":"Joao Quiterio, R. Prada, Francisco S. Melo","doi":"10.1109/CIG.2015.7317938","DOIUrl":"https://doi.org/10.1109/CIG.2015.7317938","url":null,"abstract":"Geometry Friends (GF) is a physics-based platform game, used in one of the AI competitions of the IEEE CIG Conference in 2013 and 2014. The game engages two characters, a circle and a rectangle, in a cooperative challenge involving collecting a set of objects in a 2D platform world. In this work, we propose a novel learning approach to the control of the circle character that circumvents the excessive specialization to the public levels in the competition observed in the other existing solutions for GF. Our approach proposes a method that partitions solving a level of GF into three sub-tasks: solving one platform (SP1), deciding the next platform to solve (SP2) and moving from one platform to another (SP3). We use reinforcement learning to solve SP1 and SP3 and a depth-first search to solve SP2. The quality of the agent implemented was measured against the performance of the winner of the Circle Track of the 2014 GF Game AI Competition, CIBot. Our results show that our agent is able to successfully overcome the over-specialization to the public levels, showing comparatively better performance on the private levels.","PeriodicalId":244862,"journal":{"name":"2015 IEEE Conference on Computational Intelligence and Games (CIG)","volume":"31 15 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":"126561072","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: 13

Emerging collective intelligence in Othello players evolved by differential evolution 《奥赛罗》中出现的集体智慧是由差异进化而来的

2015 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2015-11-05 DOI: 10.1109/CIG.2015.7317954

T. Takahama, S. Sakai

引用次数: 2

Creating efficient walls using potential fields in real-time strategy games 在即时战略游戏中使用势场创造有效的墙壁

2015 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2015-11-05 DOI: 10.1109/CIG.2015.7317921

Caio Freitas de Oliveira, C. Madeira

引用次数: 4

Interpreting behaviors of mobile game players from in-game data and context logs 从游戏内部数据和情境日志中解读手机游戏玩家的行为

2015 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2015-11-05 DOI: 10.1109/CIG.2015.7317895

S. Farooq, Jong-Woong Baek, Kyung-Joong Kim

引用次数: 4

Body motion design and analysis for fighting game interface 格斗游戏界面的身体动作设计与分析

2015 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2015-11-05 DOI: 10.1109/CIG.2015.7317960

Pujana Paliyawan, Kingkarn Sookhanaphibarn, Worawat Choensawat, R. Thawonmas

引用次数: 10

Evaluating Go game records for prediction of player attributes 评估围棋游戏记录以预测棋手属性

2015 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2015-11-05 DOI: 10.1109/CIG.2015.7317909

J. Moudrík, P. Baudis, Roman Neruda

引用次数: 4

Emergent bluffing and inference with Monte Carlo Tree Search 蒙特卡罗树搜索的紧急虚张声势和推理

2015 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2015-11-05 DOI: 10.1109/CIG.2015.7317927

P. Cowling, D. Whitehouse, E. Powley

{"title":"Emergent bluffing and inference with Monte Carlo Tree Search","authors":"P. Cowling, D. Whitehouse, E. Powley","doi":"10.1109/CIG.2015.7317927","DOIUrl":"https://doi.org/10.1109/CIG.2015.7317927","url":null,"abstract":"In many card and board games, players cannot see the whole game state, with different players seeing different parts of the state. In such games, gathering of information (inference) is a key strategic aspect, to which information hiding (bluffing, among other techniques) is an important countermeasure. Monte Carlo Tree Search (MCTS) is a powerful general-purpose technique for decision making in games. MCTS rose to prominence through successes in combinatorial board games such as Go, but more recently has demonstrated promise in card, board and video games of incomplete information. MCTS can construct robust plans in stochastic environments (making it strong in some games), but in its vanilla form is unable to infer or bluff (making it weak in games where this is a central feature). In this paper, we augment MCTS with mechanisms for performing inference and bluffing. Like all algorithms based on game tree search, MCTS implicitly constructs a model of the opponents' decision processes. We show that this model can be repurposed to perform an approximation of Bayesian inference. We also obtain bluffing behaviour by self-determinization (introducing “impossible” worlds into the agent's pool of sampled states). We test our algorithms on The Resistance, a popular card game based around hidden roles.","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":"128343187","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: 9