Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment最新文献_第10页

A Hierarchical System for Autonomous Musical Creation 自主音乐创作的层次体系

Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Pub Date : 2021-06-29 DOI: 10.1609/aiide.v10i5.12769

M. Reimer, Guy E. Garnett

引用次数: 2

Objective 客观的

Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Pub Date : 2021-06-29 DOI: 10.1609/aiide.v10i4.12762

Noriko Tomuro, K. Boyer, Yun-Gyung Cheong

引用次数: 0

Organization 组织

Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Pub Date : 2021-06-29 DOI: 10.1609/aiide.v10i1.12699

I. Horswill

引用次数: 0

MimicA: A Framework for Self-Learning Companion AI Behavior MimicA:一个自我学习同伴AI行为的框架

Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Pub Date : 2021-06-25 DOI: 10.1609/aiide.v12i2.12908

Travis Angevine, Foaad Khosmood

{"title":"MimicA: A Framework for Self-Learning Companion AI Behavior","authors":"Travis Angevine, Foaad Khosmood","doi":"10.1609/aiide.v12i2.12908","DOIUrl":"https://doi.org/10.1609/aiide.v12i2.12908","url":null,"abstract":"\u0000 \u0000 We explore fully autonomous companion characters within the context of Real Time Strategy games. Non-player Characters that are controlled by Artificial Intelligence to some degree, have been a feature of Role Playing games for decades. But RTS games rarely have a player avatar, and thus no real companions. The universe of RTS games where both an avatar and a companion character exist is small. Most friendly RTS units are semi-autonomous at best, requiring player micromanagement of their behavior. We present MimicA, a real-time framework to govern AI companion behavior by modeling that of the current player. Built for the Unity engine, MimicA is a learn-by-demonstration framework that differs from existing practices in that the behavior is fully autonomous, does not rely on previous modeling exercises and is designed to be generalized and extensible. We analyze and discuss MimicA through a thirty person user study with our own demonstration game, Lord of Towers. We find that 22 out of 30 participants (73%) indicate they enjoyed the game, and this self-reported enjoyment was on par with “traditional tower defense games”. 63% agree that MimicA controlled NPCs are doing what the player would do while 20% disagree. Similarly, 53% realize the NPCs are learning from the player while 20% do not. We also show that NPC with underlying Decision Tree and Naive Bayes algorithms are better than KNN in making the player realize the learning nature of the NPC.\u0000 \u0000","PeriodicalId":249108,"journal":{"name":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","volume":"520 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123060816","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

StarCraft Winner Prediction 星际争霸冠军预测

Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Pub Date : 2021-06-25 DOI: 10.1609/aiide.v12i2.12887

Yaser Norouzzadeh Ravari, S. Bakkes, P. Spronck

{"title":"StarCraft Winner Prediction","authors":"Yaser Norouzzadeh Ravari, S. Bakkes, P. Spronck","doi":"10.1609/aiide.v12i2.12887","DOIUrl":"https://doi.org/10.1609/aiide.v12i2.12887","url":null,"abstract":"\u0000 \u0000 In game-playing, a challenging topic is to investigate an evaluation function that accurately predicts which player will be the winner of a two-player match. Our work investigates to what extent it is possible to predict the winner of a StarCraft match, regardless of the races that are involved. We developed models for individual match types, and also general models for predicting the winner of non-symmetric matches, symmetric matches, and general matches. The contribution of this paper is (1) a generic and relatively accurate model for winner prediction in StarCraft, and (2) a detailed analysis of which features are the principal component in accurately predicting the winner in this complex game. Specially, our results show that we can predict the winner of a match with an accuracy of more than 63% in average over all time slices, regardless of the time slice and the combination of the match types. A study of which features are most important for the prediction of the match results, shows that the economic aspects of StarCraft matches are the strongest predictors for winning, followed by the use micro commands.\u0000 \u0000","PeriodicalId":249108,"journal":{"name":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127161318","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: 15

Single Believe State Generation for Handling Partial Observability with MCTS in StarCraft 在《星际争霸》中使用MCTS处理部分可观测性的单信念状态生成

Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Pub Date : 2021-06-25 DOI: 10.1609/aiide.v13i2.12960

Alberto Uriarte, Santiago Ontañón

引用次数: 0

"The Long Walk" From Linear Film to Interactive Narrative 从线性电影到互动叙事的“漫漫长路”

Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Pub Date : 2021-06-25 DOI: 10.1609/aiide.v13i2.12998

Keshav Prasad, K. Briët, Obiageli Odimegwu, Olivia Connolly, Diego Gonzalez, A. Gordon

{"title":"\"The Long Walk\" From Linear Film to Interactive Narrative","authors":"Keshav Prasad, K. Briët, Obiageli Odimegwu, Olivia Connolly, Diego Gonzalez, A. Gordon","doi":"10.1609/aiide.v13i2.12998","DOIUrl":"https://doi.org/10.1609/aiide.v13i2.12998","url":null,"abstract":"\u0000 \u0000 Advances in hardware and software for virtual reality and 360-degree video afford new opportunities for immersive digital storytelling, but also pose new challenges as players seek an increased sense of meaningful agency in fictional story-worlds. In this paper, we explore the interaction designs afforded by voice-controlled interactive narratives, where players speak their intended actions when prompted at choice points in branching storylines. We describe seven interaction design patterns that balance the player's need for meaningful agency with an author's goal to present an intended storyline. We argue that these structural designs are orthogonal to the content of a story, such that any particular story may be effectively restructured to use different patterns. By way of demonstration, we describe our efforts to remix and restructure a 360-degree film entitled \"The Long Walk\", transforming it from a largely linear narrative with minimal interactivity into a voice-controlled interactive narrative with meaningful player agency.\u0000 \u0000","PeriodicalId":249108,"journal":{"name":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127033293","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: 2

Experiments on Learning Unit-Action Models from Replay Data from RTS Games 基于RTS游戏重玩数据的单位动作模型学习实验

Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Pub Date : 2021-06-25 DOI: 10.1609/aiide.v12i2.12888

Santiago Ontañón

引用次数: 1

Toward Automated Story Generation with Markov Chain Monte Carlo Methods and Deep Neural Networks 基于马尔可夫链蒙特卡罗方法和深度神经网络的自动故事生成

Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Pub Date : 2021-06-25 DOI: 10.1609/aiide.v13i2.13003

Brent Harrison, Chris Purdy, Mark O. Riedl

引用次数: 31

Predicting Resource Locations in Game Maps Using Deep Convolutional Neural Networks 使用深度卷积神经网络预测游戏地图中的资源位置

Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Pub Date : 2021-06-25 DOI: 10.1609/aiide.v12i2.12893

Scott Lee, Aaron Isaksen, Christoffer Holmgård, J. Togelius

引用次数: 9