2016 IEEE Conference on Computational Intelligence and Games (CIG)最新文献_第2页

Intrinsically motivated reinforcement learning: A promising framework for procedural content generation 内在动机强化学习:程序内容生成的一个有前途的框架

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/CIG.2016.7860450

Noor Shaker

{"title":"Intrinsically motivated reinforcement learning: A promising framework for procedural content generation","authors":"Noor Shaker","doi":"10.1109/CIG.2016.7860450","DOIUrl":"https://doi.org/10.1109/CIG.2016.7860450","url":null,"abstract":"So far, Evolutionary Algorithms (EA) have been the dominant paradigm for Procedural Content Generation (PCG). While we believe the field has achieved a remarkable success, we claim that there is a wide window for improvement. The field of machine learning has an abundance of methods that promise solutions to some aspects of PCG that are still under-researched. In this paper, we advocate the use of Intrinsically motivated reinforcement learning for content generation. A class of methods that thrive for knowledge for its own sake rather than as a step towards finding a solution. We argue that this approach promises solutions to some of the well-known problems in PCG: (1) searching for novelty and diversity can be easily incorporated as an intrinsic reward, (2) improving models of player experience and generation of adapted content can be done simultaneously through combining extrinsic and intrinsic rewards, and (3) mix-initiative design tools can incorporate more knowledge about the designer and her preferences and ultimately provide better assistance. We demonstrate our arguments and discuss the challenges that face the proposed approach.","PeriodicalId":6594,"journal":{"name":"2016 IEEE Conference on Computational Intelligence and Games (CIG)","volume":"128 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":"88700978","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

Using opponent models to train inexperienced synthetic agents in social environments 使用对手模型在社会环境中训练没有经验的人工智能体

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/CIG.2016.7860409

C. Kiourt, Dimitris Kalles

引用次数: 4

Investigating vanilla MCTS scaling on the GVG-AI game corpus 在GVG-AI游戏语料库上研究香草MCTS缩放

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/CIG.2016.7860443

M. Nelson

{"title":"Investigating vanilla MCTS scaling on the GVG-AI game corpus","authors":"M. Nelson","doi":"10.1109/CIG.2016.7860443","DOIUrl":"https://doi.org/10.1109/CIG.2016.7860443","url":null,"abstract":"The General Video Game AI Competition (GVG-AI) invites submissions of controllers to play games specified in the Video Game Description Language (VGDL), testing them against each other and several baselines. One of the baselines that has done surprisingly well in some of the competitions is sampleMCTS, a straightforward implementation of Monte Carlo tree search (MCTS). Although it has done worse in other iterations of the competition, this has produced a nagging worry to us that perhaps the GVG-AI competition might be too easy, especially since performance profiling suggests that significant increases in number of MCTS iterations that can be completed in a given time limit will be possible through optimizations to the GVG-AI competition framework. To better understand the potential performance of the baseline vanilla MCTS controller, I perform scaling experiments, running it against the 62 games in the public GVG-AI corpus as the time budget is varied from about 1/30 of that in the current competition, through around 30x the current competition's budget. I find that it does not in fact master the games even given 30x the current time budget, so the challenge of the GVG-AI competition is safe (at least against this baseline). However, I do find that given enough computational budget, it manages to avoid explicitly losing on most games, despite failing to win them and ultimately losing as time expires, suggesting an asymmetry in the current GVG-AI competition's challenge: not losing is significantly easier than winning.","PeriodicalId":6594,"journal":{"name":"2016 IEEE Conference on Computational Intelligence and Games (CIG)","volume":"186 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76859933","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: 27

Modeling believable game characters 塑造可信的游戏角色

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/CIG.2016.7860412

Hanneke Kersjes, P. Spronck

{"title":"Modeling believable game characters","authors":"Hanneke Kersjes, P. Spronck","doi":"10.1109/CIG.2016.7860412","DOIUrl":"https://doi.org/10.1109/CIG.2016.7860412","url":null,"abstract":"The behavior of virtual characters in computer games is usually determined solely by decision trees or finite state machines, which is detrimental to the characters' believability. It has been argued that enhancing the virtual characters with emotions, personalities, and moods, may make their behavior more diverse and thus more believable. Most research in this direction is based on existing (socio-)psychological literature, but not tested in a suitable experimental setting where humans interact with the virtual characters. In our research, we use a simplified version of the personality model of Ochs et al. [1], which we test in a game which has human participants interact with three agents with different personalities: an extraverted agent, a neurotic agent, and a neutral agent. The model only influences the agents' emotions, which are only exhibited by their facial expressions. The participants were asked to assess the agents' personality based on six possible traits. We found that the participants considered the neurotic agent as the most neurotic, while there are also indications that the extraverted agent was considered the most extraverted. We conclude that players will indeed distinguish personality differences between agents based on their facial expression of emotions. Therefore, using a personality model may make it easy for game developers to quickly create a high variety of virtual characters, who exhibit individual behaviors, making them more believable.","PeriodicalId":6594,"journal":{"name":"2016 IEEE Conference on Computational Intelligence and Games (CIG)","volume":"159 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":"77208657","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

Heterogeneous team deep q-learning in low-dimensional multi-agent environments 低维多智能体环境下的异构团队深度q学习

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/CIG.2016.7860413

Mateusz Kurek, Wojciech Jaśkowski

{"title":"Heterogeneous team deep q-learning in low-dimensional multi-agent environments","authors":"Mateusz Kurek, Wojciech Jaśkowski","doi":"10.1109/CIG.2016.7860413","DOIUrl":"https://doi.org/10.1109/CIG.2016.7860413","url":null,"abstract":"Deep Q-Learning is an effective reinforcement learning method, which has recently obtained human-level performance for a set of Atari 2600 games. Remarkably, the system was trained on the high-dimensional raw visual data. Is Deep Q-Learning equally valid for problems involving a low-dimensional state space? To answer this question, we evaluate the components of Deep Q-Learning (deep architecture, experience replay, target network freezing, and meta-state) on a Keepaway soccer problem, where the state is described only by 13 variables. The results indicate that although experience replay indeed improves the agent performance, target network freezing and meta-state slow down the learning process. Moreover, the deep architecture does not help for this task since a rather shallow network with just two hidden layers worked the best. By selecting the best settings, and employing heterogeneous team learning, we were able to outperform all previous methods applied to Keepaway soccer using a fraction of the runner-up's computational expense. These results extend our understanding of the Deep Q-Learning effectiveness for low-dimensional reinforcement learning tasks.","PeriodicalId":6594,"journal":{"name":"2016 IEEE Conference on Computational Intelligence and Games (CIG)","volume":"74 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":"85188453","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: 20

Semi-automated level design via auto-playtesting for handheld casual game creation 基于自动测试的半自动关卡设计，适用于手持休闲游戏制作

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/CIG.2016.7860438

E. Powley, S. Colton, Swen E. Gaudl, Rob Saunders, M. Nelson

引用次数: 12

MCTS/EA hybrid GVGAI players and game difficulty estimation MCTS/EA混合GVGAI玩家和游戏难度估算

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/CIG.2016.7860384

Hendrik Horn, Vanessa Volz, Diego Perez Liebana, M. Preuss

引用次数: 23

Artefacts: Minecraft meets collaborative interactive evolution 人工制品:《我的世界》符合协作互动进化

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/CIG.2016.7860434

Cristinel Patrascu, S. Risi

引用次数: 9

Intrinsically motivated general companion NPCs via Coupled Empowerment Maximisation 内在动机一般同伴npc通过耦合授权最大化

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/CIG.2016.7860406

C. Guckelsberger, Christoph Salge, S. Colton

引用次数: 23

Evolving micro for 3D Real-Time Strategy games 3D即时策略游戏的进化微系统

2016 IEEE Conference on Computational Intelligence and Games (CIG) Pub Date : 2016-09-01 DOI: 10.1109/CIG.2016.7860437

T. DeWitt, S. Louis, Siming Liu

引用次数: 4