2022 IEEE Conference on Games (CoG)最新文献_第10页

Surrogate Infeasible Fitness Acquirement FI-2Pop for Procedural Content Generation 程序内容生成的替代不可行适应度获取FI-2Pop

2022 IEEE Conference on Games (CoG) Pub Date : 2022-05-12 DOI: 10.1109/CoG51982.2022.9893592

R. Gallotta, Kai Arulkumaran, L. Soros

引用次数: 4

On the Verge of Solving Rocket League using Deep Reinforcement Learning and Sim-to-sim Transfer 即将使用深度强化学习和模拟到模拟迁移来解决火箭联盟

2022 IEEE Conference on Games (CoG) Pub Date : 2022-05-10 DOI: 10.1109/CoG51982.2022.9893628

Marco Pleines, Konstantin Ramthun, Yannik Wegener, Hendrik Meyer, M. Pallasch, Sebastian Prior, Jannik Drögemüller, Leon Büttinghaus, Thilo Röthemeyer, Alexander Kaschwig, Oliver Chmurzynski, Frederik Rohkrähmer, Roman Kalkreuth, F. Zimmer, M. Preuss

引用次数: 1

PGD: A Large-scale Professional Go Dataset for Data-driven Analytics PGD:用于数据驱动分析的大规模专业围棋数据集

2022 IEEE Conference on Games (CoG) Pub Date : 2022-04-30 DOI: 10.1109/CoG51982.2022.9893704

Yifan Gao

{"title":"PGD: A Large-scale Professional Go Dataset for Data-driven Analytics","authors":"Yifan Gao","doi":"10.1109/CoG51982.2022.9893704","DOIUrl":"https://doi.org/10.1109/CoG51982.2022.9893704","url":null,"abstract":"Lee Sedol is on a winning streak–does this legend rise again after the competition with AlphaGo? Ke Jie is invincible in the world championship–can he still win the title this time? Go is one of the most popular board games in East Asia, with a stable professional sports system that has lasted for decades in China, Japan, and Korea. There are mature data-driven analysis technologies for many sports, such as soccer, basketball, and esports. However, developing such technology for Go remains nontrivial and challenging due to the lack of datasets, meta-information, and in-game statistics. This paper creates the Professional Go Dataset (PGD), containing 98,043 games played by 2,148 professional players from 1950 to 2021. After manual cleaning and labeling, we provide detailed meta-information for each player, game, and tournament. Moreover, the dataset includes analysis results for each move in the match evaluated by advanced AlphaZero-based AI. To establish a benchmark for PGD, we further analyze the data and extract meaningful in-game features based on prior knowledge related to Go that can indicate the game status. With the help of complete meta-information and constructed in-game features, our results prediction system achieves an accuracy of 75.30%, much higher than several state-of-the-art approaches (64%-65%). As far as we know, PGD is the first dataset for data-driven analytics in Go and even in board games. Beyond this promising result, we provide more examples of tasks that benefit from our dataset. The ultimate goal of this paper is to bridge this ancient game and the modern data science community. It will advance research on Go-related analytics to enhance the fan experience, help players improve their ability, and facilitate other promising aspects. The dataset will be made publicly available.","PeriodicalId":394281,"journal":{"name":"2022 IEEE Conference on Games (CoG)","volume":"175 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132527163","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

Using Wordle for Learning to Design and Compare Strategies 使用世界学习设计和比较策略

2022 IEEE Conference on Games (CoG) Pub Date : 2022-04-30 DOI: 10.1109/CoG51982.2022.9893585

Chao-Lin Liu

引用次数: 7

DouZero+: Improving DouDizhu AI by Opponent Modeling and Coach-guided Learning DouZero+:通过对手建模和教练引导学习来改进斗猪猪AI

2022 IEEE Conference on Games (CoG) Pub Date : 2022-04-06 DOI: 10.1109/CoG51982.2022.9893710

Youpeng Zhao, Jian Zhao, Xu Hu, Wen-gang Zhou, Houqiang Li

引用次数: 3

On Linking Level Segments 关于水平段的连接

2022 IEEE Conference on Games (CoG) Pub Date : 2022-03-09 DOI: 10.1109/CoG51982.2022.9893705

Colan F. Biemer, Seth Cooper

引用次数: 4

DareFightingICE Competition: A Fighting Game Sound Design and AI Competition DareFightingICE竞赛:格斗游戏声音设计和AI竞赛

2022 IEEE Conference on Games (CoG) Pub Date : 2022-03-03 DOI: 10.1109/CoG51982.2022.9893624

Ibrahim Khan, T. Nguyen, Xincheng Dai, R. Thawonmas

{"title":"DareFightingICE Competition: A Fighting Game Sound Design and AI Competition","authors":"Ibrahim Khan, T. Nguyen, Xincheng Dai, R. Thawonmas","doi":"10.1109/CoG51982.2022.9893624","DOIUrl":"https://doi.org/10.1109/CoG51982.2022.9893624","url":null,"abstract":"This paper presents a new competition-at the 2022 IEEE Conference on Games (CoG)- called DareFightingICE Competition. The competition has two tracks: a sound design track and an AI track. The game platform for this competition is also called DareFightingICE, a fighting game platform. DareFightingICE is a sound-design-enhanced version of FightingICE, used earlier in a competition at CoG until 2021 to promote artificial intelligence (AI) research in fighting games. In the sound design track, participants compete for the best sound design, given the default sound design of DareFightingICE as a sample, where we define a sound design as a set of sound effects combined with the source code that implements their timing-control algorithm. Participants of the AI track are asked to develop their AI algorithm that controls a character given only sound as the input (blind AI) to fight against their opponent; a sample deep-learning blind AI will be provided by us. Our means to maximize the synergy between the two tracks are also described. This competition serves to come up with effective sound designs for visually impaired players, a group in the gaming community which has been mostly ignored. To the best of our knowledge, DareFightingICE Competition is the first of its kind within and outside of CoG.","PeriodicalId":394281,"journal":{"name":"2022 IEEE Conference on Games (CoG)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128347913","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: 6

Illuminating the Space of Enemies Through MAP-Elites 通过地图精英照亮敌人的空间

2022 IEEE Conference on Games (CoG) Pub Date : 2022-02-19 DOI: 10.1109/CoG51982.2022.9893621

Breno M. F. Viana, L. T. Pereira, C. Toledo

引用次数: 3

Counter-Strike Deathmatch with Large-Scale Behavioural Cloning 基于大规模行为克隆的《反恐精英》死亡竞赛

2022 IEEE Conference on Games (CoG) Pub Date : 2021-04-09 DOI: 10.1109/CoG51982.2022.9893617

Tim Pearce, Jun Zhu

{"title":"Counter-Strike Deathmatch with Large-Scale Behavioural Cloning","authors":"Tim Pearce, Jun Zhu","doi":"10.1109/CoG51982.2022.9893617","DOIUrl":"https://doi.org/10.1109/CoG51982.2022.9893617","url":null,"abstract":"This paper describes an AI agent that plays the modern first-person-shooter (FPS) video game ‘Counter-Strike; Global Offensive’ (CSGO) from pixel input. The agent, a deep neural network, matches the performance of a casual human gamer on the deathmatch game mode whilst adopting a humanlike play style. Much previous research has focused on games with convenient APIs and low-resolution graphics, allowing them to be run cheaply at scale. This is not the case for CSGO, with system requirements orders of magnitude higher than previously studied FPS games. This limits the quantity of on-policy data that can be generated, precluding pure reward-driven reinforcement learning (RL) algorithms. Our solution uses a two-stage behavioural cloning methodology; 1) Pre-train on a large dataset scraped from human play on public servers (5.5 million frames or 95 hours) where actions are labelled in an automated way. 2) Fine-tune on a small dataset of clean expert demonstrations (190 thousand frames or 3 hours). This scale is an order of magnitude larger than prior work on imitation learning in FPS games, whilst being far more data efficient than pure RL algorithms. Video introduction: https://youtu.be/rnz3lmfSHv0 Code, model & datasets: https://github.com/TeaPearce","PeriodicalId":394281,"journal":{"name":"2022 IEEE Conference on Games (CoG)","volume":"96 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126096621","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: 16