Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment最新文献_第2页

Risk Management: Anticipating and Reacting in StarCraft 风险管理:《星际争霸》中的预测和反应

Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Pub Date : 2023-10-06 DOI: 10.1609/aiide.v19i1.27497

Adam Amos-Binks, Bryan S. Weber

引用次数: 0

MultiStyle: Characterizing Multiplayer Cooperative Gameplay by Incorporating Distinct Player Playstyles in a Multi-Agent Planner MultiStyle:通过在Multi-Agent Planner中整合不同的玩家游戏风格来描述多人合作游戏玩法

Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Pub Date : 2023-10-06 DOI: 10.1609/aiide.v19i1.27505

Eric W. Lang, R. Michael Young

引用次数: 0

Enhancing Character Depth through Personality Exceptions for Narrative Planners 通过叙述计划者的个性例外来增强角色深度

Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Pub Date : 2023-10-06 DOI: 10.1609/aiide.v19i1.27509

Elinor Rubin-McGregor, Brent Harrison, Cory Siler

引用次数: 0

Learning of Generalizable and Interpretable Knowledge in Grid-Based Reinforcement Learning Environments 基于网格的强化学习环境中可推广和可解释知识的学习

Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Pub Date : 2023-10-06 DOI: 10.1609/aiide.v19i1.27516

Manuel Eberhardinger, Johannes Maucher, Setareh Maghsudi

{"title":"Learning of Generalizable and Interpretable Knowledge in Grid-Based Reinforcement Learning Environments","authors":"Manuel Eberhardinger, Johannes Maucher, Setareh Maghsudi","doi":"10.1609/aiide.v19i1.27516","DOIUrl":"https://doi.org/10.1609/aiide.v19i1.27516","url":null,"abstract":"Understanding the interactions of agents trained with deep reinforcement learning is crucial for deploying agents in games or the real world. In the former, unreasonable actions confuse players. In the latter, that effect is even more significant, as unexpected behavior cause accidents with potentially grave and long-lasting consequences for the involved individuals. In this work, we propose using program synthesis to imitate reinforcement learning policies after seeing a trajectory of the action sequence. Programs have the advantage that they are inherently interpretable and verifiable for correctness. We adapt the state-of-the-art program synthesis system DreamCoder for learning concepts in grid-based environments, specifically, a navigation task and two miniature versions of Atari games, Space Invaders and Asterix. By inspecting the generated libraries, we can make inferences about the concepts the black-box agent has learned and better understand the agent's behavior. We achieve the same by visualizing the agent's decision-making process for the imitated sequences. We evaluate our approach with different types of program synthesizers based on a search-only method, a neural-guided search, and a language model fine-tuned on code.","PeriodicalId":498041,"journal":{"name":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","volume":"169 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135303527","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

SceneCraft: Automating Interactive Narrative Scene Generation in Digital Games with Large Language Models SceneCraft:在大型语言模型的数字游戏中自动生成交互式叙事场景

Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Pub Date : 2023-10-06 DOI: 10.1609/aiide.v19i1.27504

Vikram Kumaran, Jonathan Rowe, Bradford Mott, James Lester

{"title":"SceneCraft: Automating Interactive Narrative Scene Generation in Digital Games with Large Language Models","authors":"Vikram Kumaran, Jonathan Rowe, Bradford Mott, James Lester","doi":"10.1609/aiide.v19i1.27504","DOIUrl":"https://doi.org/10.1609/aiide.v19i1.27504","url":null,"abstract":"Creating engaging interactive story-based experiences dynamically responding to individual player choices poses significant challenges for narrative-centered games. Recent advances in pre-trained large language models (LLMs) have the potential to revolutionize procedural content generation for narrative-centered games. Historically, interactive narrative generation has specified pivotal events in the storyline, often utilizing planning-based approaches toward achieving narrative coherence and maintaining the story arc. However, manual authorship is typically used to create detail and variety in non-player character (NPC) interaction to specify and instantiate plot events. This paper proposes SCENECRAFT, a narrative scene generation framework that automates NPC interaction crucial to unfolding plot events. SCENECRAFT interprets natural language instructions about scene objectives, NPC traits, location, and narrative variations. It then employs large language models to generate game scenes aligned with authorial intent. It generates branching conversation paths that adapt to player choices while adhering to the author’s interaction goals. LLMs generate interaction scripts, semantically extract character emotions and gestures to align with the script, and convert dialogues into a game scripting language. The generated script can then be played utilizing an existing narrative-centered game framework. Through empirical evaluation using automated and human assessments, we demonstrate SCENECRAFT’s effectiveness in creating narrative experiences based on creativity, adaptability, and alignment with intended author instructions.","PeriodicalId":498041,"journal":{"name":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135303355","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

Tree-Based Reconstructive Partitioning: A Novel Low-Data Level Generation Approach 基于树的重构分区:一种新的低数据级生成方法

Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Pub Date : 2023-10-06 DOI: 10.1609/aiide.v19i1.27520

Emily Halina, Matthew Guzdial

引用次数: 0

Are You Talking to Me? A Case Study in Emotional Human-Machine Interaction 你在跟我说话吗?情感人机交互的案例研究

Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Pub Date : 2023-10-06 DOI: 10.1609/aiide.v19i1.27538

Manuel Flurin Hendry, Norbert Kottmann, Martin Fröhlich, Florian Bruggisser, Marco Quandt, Stella Speziali, Valentin Huber, Chris Salter

引用次数: 0

There and Back Again: Extracting Formal Domains for Controllable Neurosymbolic Story Authoring 来去自如:提取可控制神经符号故事创作的形式域

Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Pub Date : 2023-10-06 DOI: 10.1609/aiide.v19i1.27502

Jack Kelly, Alex Calderwood, Noah Wardrip-Fruin, Michael Mateas

引用次数: 0

Investigating the Influence of Behaviors and Dialogs on Player Enjoyment in Stealth Games 研究潜行游戏中行为和对话对玩家乐趣的影响

Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Pub Date : 2023-10-06 DOI: 10.1609/aiide.v19i1.27512

Wael Al Enezi, Clark Verbrugge

{"title":"Investigating the Influence of Behaviors and Dialogs on Player Enjoyment in Stealth Games","authors":"Wael Al Enezi, Clark Verbrugge","doi":"10.1609/aiide.v19i1.27512","DOIUrl":"https://doi.org/10.1609/aiide.v19i1.27512","url":null,"abstract":"The player's perception of AI behavior significantly influences their overall game experience. This perception is shaped by both interactive encounters and careful observations, particularly in genres like stealth, where gameplay revolves around planning strategies based on AI enemy movement. This paper aims to derive general insights into the player experience concerning two crucial gameplay elements that impact the perception of NPC intelligence. The first element pertains to the actual behavior of opponent NPCs, while the second focuses on the dialogues employed to highlight NPC decision-making. We conducted a user study to assess whether players can discern between complex and simple NPC behavior during gameplay in a specific scenario of a top-down stealth game prototype. We introduced variations in spoken dialogs to determine their effect on player perception. In the end, our findings revealed that when simple dialogs were used, players derived greater enjoyment from a more complex AI behavior. However, using contextual dialog allowed a simple behavior to match a complex behavior in player enjoyment.","PeriodicalId":498041,"journal":{"name":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135303360","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

Evolving Interactive Narrative Worlds 不断发展的互动叙事世界

Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Pub Date : 2023-10-06 DOI: 10.1609/aiide.v19i1.27508

Justus Robertson, John Heiden, Rogelio E. Cardona-Rivera

引用次数: 0