2016 IEEE Conference on Computational Intelligence and Games (CIG)最新文献_第4页

Planning social actions through the others' eyes for emergent storytelling 通过他人的视角来规划社会行动，从而形成突发故事

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/CIG.2016.7860400

D. Carvalho, E. Clua, A. Paes

{"title":"Planning social actions through the others' eyes for emergent storytelling","authors":"D. Carvalho, E. Clua, A. Paes","doi":"10.1109/CIG.2016.7860400","DOIUrl":"https://doi.org/10.1109/CIG.2016.7860400","url":null,"abstract":"Stories have become an important element of games, since they can increase their immersion level by giving the players the context and the motivation to play. However, despite the interactive nature of games, their stories usually do not develop considering every decision and/or action the players are capable of, because depending on the game size, it would take too much effort to author alternative routes for all of them. To make these alternatives viable, an interesting solution would be to procedurally generate them, which could be achieved by using the story generation approaches already developed by many works of the storytelling field. Some of these approaches are based on the simulation of virtual worlds, in which the stories are generated by making the characters that inhabit the worlds act trying to reach their goals. The resulting actions and the world's reactions compose the final story. Since the actions are the building blocks of the stories, the characters' acting capabilities are determinant features of the generation potential of simulations. For instance, it is only possible to generate stories with deception if the characters are capable of deceiving each other. To allow the generation of stories where the characters are capable of manipulation, cooperation and other social behaviors by actively using what the others will do based on what they know and see, we propose a recursive planning approach that deals with the uncertainty of the others' knowledge and with a purposely error-prone perception simulation. To test our proposal we developed a story generation system and designed an adaptation of Little Red Riding Hood world as test scenario. With our approach, the system was capable of generating coherent story variations with deceptive actions.","PeriodicalId":6594,"journal":{"name":"2016 IEEE Conference on Computational Intelligence and Games (CIG)","volume":"37 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":"83326351","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

Transfer learning for cross-game prediction of player experience 跨游戏预测玩家体验的迁移学习

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/CIG.2016.7860415

Noor Shaker, Mohamed Abou-Zleikha

引用次数: 10

Evolutionary deckbuilding in hearthstone 《炉石传说》中的进化甲板建造

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/CIG.2016.7860426

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

引用次数: 42

Design influence on player retention: A method based on time varying survival analysis 设计对玩家留存率的影响:基于时变生存分析的方法

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/CIG.2016.7860421

Thibault Allart, G. Levieux, M. Pierfitte, Agathe Guilloux, S. Natkin

引用次数: 9

Position-based reinforcement learning biased MCTS for General Video Game Playing 基于位置的强化学习偏向MCTS的一般视频游戏

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/CIG.2016.7860449

C. Chu, Suguru Ito, Tomohiro Harada, R. Thawonmas

引用次数: 5

Pruning and preprocessing methods for inventory-aware pathfinding 库存感知寻路的修剪和预处理方法

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/CIG.2016.7860417

D. Aversa, Sebastian Sardiña, S. Vassos

引用次数: 0

Monte-Carlo simulation balancing revisited 蒙特卡罗模拟平衡重新审视

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/CIG.2016.7860411

Tobias Graf, M. Platzner

引用次数: 4

Ms. Pac-Man Versus Ghost Team CIG 2016 competition 吃豆人小姐对战幽灵队CIG 2016比赛

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/CIG.2016.7860446

P. R. Williams, Diego Perez Liebana, S. Lucas

引用次数: 28

Recovering visibility and dodging obstacles in pursuit-evasion games 在追逐逃避游戏中恢复能见度和躲避障碍物

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/CIG.2016.7860419

Ahmed Abdelkader

引用次数: 0

Monte Carlo Tree Search with options for general video game playing 蒙特卡洛树搜索与选项一般视频游戏玩

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/CIG.2016.7860383

M. D. Waard, Diederik M. Roijers, S. Bakkes

{"title":"Monte Carlo Tree Search with options for general video game playing","authors":"M. D. Waard, Diederik M. Roijers, S. Bakkes","doi":"10.1109/CIG.2016.7860383","DOIUrl":"https://doi.org/10.1109/CIG.2016.7860383","url":null,"abstract":"General video game playing is a challenging research area in which the goal is to find one algorithm that can play many games successfully. “Monte Carlo Tree Search” (MCTS) is a popular algorithm that has often been used for this purpose. It incrementally builds a search tree based on observed states after applying actions. However, the MCTS algorithm always plans over actions and does not incorporate any higher level planning, as one would expect from a human player. Furthermore, although many games have similar game dynamics, often no prior knowledge is available to general video game playing algorithms. In this paper, we introduce a new algorithm called “Option Monte Carlo Tree Search” (O-MCTS). It offers general video game knowledge and high level planning in the form of “options”, which are action sequences aimed at achieving a specific subgoal. Additionally, we introduce “Option Learning MCTS” (OL-MCTS), which applies a progressive widening technique to the expected returns of options in order to focus exploration on fruitful parts of the search tree. Our new algorithms are compared to MCTS on a diverse set of twenty-eight games from the general video game AI competition. Our results indicate that by using MCTS's efficient tree searching technique on options, O-MCTS outperforms MCTS on most of the games, especially those in which a certain subgoal has to be reached before the game can be won. Lastly, we show that OL-MCTS improves its performance on specific games by learning expected values for options and moving a bias to higher valued options.","PeriodicalId":6594,"journal":{"name":"2016 IEEE Conference on Computational Intelligence and Games (CIG)","volume":"122 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":"88114215","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