Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment最新文献_第4页

Praxish: A Rational Reconstruction of a Logic-Based DSL for Modeling Social Practices 实践:基于逻辑的社会实践建模DSL的理性重构

Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Pub Date : 2023-10-06 DOI: 10.1609/aiide.v19i1.27537

James Dameris, Rosaura Hernandez Roman, Max Kreminski

引用次数: 0

FlexComb: A Facial Landmark-Based Model for Expression Combination Generation FlexComb:一种基于面部地标的表情组合生成模型

Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Pub Date : 2023-10-06 DOI: 10.1609/aiide.v19i1.27529

Bogdan Pikula, Steve Engels

{"title":"FlexComb: A Facial Landmark-Based Model for Expression Combination Generation","authors":"Bogdan Pikula, Steve Engels","doi":"10.1609/aiide.v19i1.27529","DOIUrl":"https://doi.org/10.1609/aiide.v19i1.27529","url":null,"abstract":"Facial expressions are a crucial but challenging aspect of animating in-game characters. They provide vital nonverbal communication cues, but given the high complexity and variability of human faces, the task of capturing the natural diversity and affective complexity of human faces can be a labour-intensive process for animators. This motivates the need for more accurate, realistic and lightweight methods for generating emotional expressions for in-game characters. In this work, we introduce FlexComb, a Facial Landmark-based Expression Combination model, designed to generate a real-time space of realistic facial expression combinations. FlexComb leverages the highly varied CelebV-HQ dataset containing emotions in the wild, and a transformer-based architecture. The central component of the FlexComb system is an emotion recognition model that is trained on the facial dataset, and used to generate a larger dataset of tagged faces. The resulting system generates in-game facial expressions by sampling from this tagged dataset, including expressions that combine emotions in specified amounts. This allows in-game characters to take on variety of realistic facial expressions for a single emotion, which addresses this primary challenge of facial emotion modeling. FlexComb shows potential for expressive facial emotion simulation with applications that include animation, video game development, virtual reality, and human-computer interaction.","PeriodicalId":498041,"journal":{"name":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","volume":"26 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":"135303668","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

Hierarchical WaveFunction Collapse 分层波函数崩溃

Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Pub Date : 2023-10-06 DOI: 10.1609/aiide.v19i1.27498

Michael Beukman, Branden Ingram, Ireton Liu, Benjamin Rosman

{"title":"Hierarchical WaveFunction Collapse","authors":"Michael Beukman, Branden Ingram, Ireton Liu, Benjamin Rosman","doi":"10.1609/aiide.v19i1.27498","DOIUrl":"https://doi.org/10.1609/aiide.v19i1.27498","url":null,"abstract":"Video game developers are increasingly utilising procedural content generation (PCG) techniques in order to generate more content far quicker than if it were designed. Although promising, much of the successful work to date has been achieved in simple 2D environments or has required significant hand-designed effort. This is due to the difficult nature of defining plausible metrics, fitness functions or reward functions which can quantify the quality of generated levels. Our work aims to avoid this difficulty by utilising minimal human design to build up constraints, and generating diverse levels that maintain these constraints. We achieve this by hierarchically applying the recent WaveFunction collapse (WFC) algorithm. Our approach allows designers to specify larger-scale components, and additional constraints that are difficult to enforce using standard WFC. We empirically demonstrate that our approach does indeed incorporate these higher-level structures, and is more controllable than our baselines. Despite these benefits, our levels do not suffer from a lack of diversity. Finally, we illustrate the scalability and flexibility of our approach by applying it to both 2D and 3D domains.","PeriodicalId":498041,"journal":{"name":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","volume":"57 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":"135303670","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

Expressive Response Curves: Testing Expressive Game Feel with A* 表达性反应曲线:用A*测试表达性游戏感觉

Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Pub Date : 2023-10-06 DOI: 10.1609/aiide.v19i1.27524

Nic Junius, Elin Carstensdottir

{"title":"Expressive Response Curves: Testing Expressive Game Feel with A*","authors":"Nic Junius, Elin Carstensdottir","doi":"10.1609/aiide.v19i1.27524","DOIUrl":"https://doi.org/10.1609/aiide.v19i1.27524","url":null,"abstract":"Designing AI models for expressive character behavior is a considerable challenge. Such models represent a massive possibility space of individual behaviors and sequences of different character expressions. Iterating on designs of such models is complex because the possibility spaces they afford are challenging to understand in their entirety and map intuitively onto a meaningful experience for a user. Automated playtesting has primarily been focused on the physical spaces of game levels and the ability of AI players to enact personas and complete tasks within those levels. However, core principles of automated playtesting can be applied to expressive models to expose information about their expressive possibility space. We propose a new approach to automated playtesting for AI character behaviors: Expressive Response Curves (ERC). ERC allows us to map specific actions taken by a player to perform a particular expression to understand the affordances of an expressive possibility space. We present a case study applying ERC to Puppitor rulesets. We show that using this method we can compile paths through Puppitor rulesets to map them and further understand the nature of the expressive spaces afforded by the system. We argue that by using ERC, it is possible to give designers more nuanced information and guidance to create better and more expressive AI characters.","PeriodicalId":498041,"journal":{"name":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","volume":"162 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":"135303347","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

Cautious Curiosity: A Novel Approach to a Human-Like Gameplay Agent 谨慎的好奇心:一种创造类人玩法代理的新方法

Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Pub Date : 2023-10-06 DOI: 10.1609/aiide.v19i1.27533

Chujin Zhou, Tiago Machado, Casper Harteveld

{"title":"Cautious Curiosity: A Novel Approach to a Human-Like Gameplay Agent","authors":"Chujin Zhou, Tiago Machado, Casper Harteveld","doi":"10.1609/aiide.v19i1.27533","DOIUrl":"https://doi.org/10.1609/aiide.v19i1.27533","url":null,"abstract":"We introduce a new reward function direction for intrinsically motivated reinforcement learning to mimic human behavior in the context of computer games. Similar to previous research, we focus on so-called ``curiosity agents'', which are agents whose intrinsic reward is based on the concept of curiosity. We designed our novel intrinsic reward, which we call ``Cautious Curiosity'' (CC) based on (1) a theory that proposes curiosity as a psychological definition called information gap, and (2) a recent study showing that the relationship between curiosity and information gap is an inverted U-curve. In this work, we compared our agent using the classic game Super Mario Bros. with (1) a random agent, (2) an agent based on the Asynchronous Advantage Actor Critic algorithm (A3C), (3) an agent based on the Intrinsic Curiosity Module (ICM), and (4) an average human player. We also asked participants (n = 100) to watch videos of these agents and rate how human-like they are. The main contribution of this work is that we present a reward function that, as perceived by humans, induces an agent to play a computer game similarly to a human, while maintaining its competitiveness and being more believable compared to other agents.","PeriodicalId":498041,"journal":{"name":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","volume":"35 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":"135303361","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

Modeling Morality-Based Argumentation for Believable Game Characters: A Design Postmortem 为可信的游戏角色建模基于道德的论证:设计事后分析

Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Pub Date : 2023-10-06 DOI: 10.1609/aiide.v19i1.27514

Rehaf AlJammaz, Michael Mateas, Noah Wardrip-Fruin

{"title":"Modeling Morality-Based Argumentation for Believable Game Characters: A Design Postmortem","authors":"Rehaf AlJammaz, Michael Mateas, Noah Wardrip-Fruin","doi":"10.1609/aiide.v19i1.27514","DOIUrl":"https://doi.org/10.1609/aiide.v19i1.27514","url":null,"abstract":"An ability to morally reason is crucial to the believability of many fictional characters, from Jane Austen’s heroines to the denizens of The Good Place. These works often foreground the complexity of moral questions and the circumstances un- der which different forms of behavior might be justified. Morality is also foregrounded in many games, from Black and White to Mass Effect 3. Yet, most in-game characters judge other characters (or the player) based on a single reputation scale or binary values of right and wrong. There has been little exploration in games of the relationship between char- acter values and beliefs and moral reasoning. In keeping with this year’s conference theme, “Oh the Humanity,” this design postmortem paper describes the design and development of Argument Box, a model of moral argumentation and reason- ing based on Lakoff’s metaphor theory of moral politics. We describe our design approach, iterations, and authoring con- cerns — covering what went right and wrong in our attempts to model morality-based argumentation for believable game characters.","PeriodicalId":498041,"journal":{"name":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","volume":"102 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":"135303669","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

Navigation in Adversarial Environments Guided by PRA* and a Local RL Planner 基于PRA*和局部RL规划器的对抗环境导航

Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Pub Date : 2023-10-06 DOI: 10.1609/aiide.v19i1.27530

Debraj Ray, Nathan R. Sturtevant

{"title":"Navigation in Adversarial Environments Guided by PRA* and a Local RL Planner","authors":"Debraj Ray, Nathan R. Sturtevant","doi":"10.1609/aiide.v19i1.27530","DOIUrl":"https://doi.org/10.1609/aiide.v19i1.27530","url":null,"abstract":"Real-time strategy games require players to respond to short-term challenges (micromanagement) and long-term objectives (macromanagement) simultaneously to win. However, many players excel at one of these skills but not both. This research is motivated by the question of whether the burden of micromanagement can be reduced on human players through delegation of responsibility to autonomous agents. In particular, this research proposes an adversarial navigation architecture that enables units to autonomously navigate through places densely populated with enemies by learning to micromanage itself. Our approach models the adversarial pathfinding problem as a Markov Decision Process (MDP) and trains an agent with reinforcement learning on this MDP. We observed that our approach resulted in the agent taking less damage from adversaries while travelling shorter paths, compared to previous approaches for adversarial navigation. Our approach is also efficient in memory use and computation time. Interestingly, the agent using the proposed approach also outperformed baseline approaches while navigating through environments that are significantly different from the training environments. Furthermore, when the game design is modified, the agent discovers effective alternate strategies considering the updated design without any changes in the learning framework. This property is particularly useful because in game development the design of a game is often updated iteratively.","PeriodicalId":498041,"journal":{"name":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","volume":"49 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":"135303232","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

Observer Rules for Box-Split Grammars 分割框语法的观察者规则

Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Pub Date : 2023-10-06 DOI: 10.1609/aiide.v19i1.27515

Nicholas Baron, Markus Eger

引用次数: 0

Playing Various Strategies in Dominion with Deep Reinforcement Learning 用深度强化学习在Dominion中玩各种策略

Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Pub Date : 2023-10-06 DOI: 10.1609/aiide.v19i1.27518

Jasper Gerigk, Steve Engels

{"title":"Playing Various Strategies in Dominion with Deep Reinforcement Learning","authors":"Jasper Gerigk, Steve Engels","doi":"10.1609/aiide.v19i1.27518","DOIUrl":"https://doi.org/10.1609/aiide.v19i1.27518","url":null,"abstract":"Deck-building games, like Dominion, present an unsolved challenge for game AI research. The complexity arising from card interactions and the relative strength of strategies depending on the game configuration result in computer agents being limited to simple strategies. This paper describes the first application of recent advances in Geometric Deep Learning to deck-building games. We utilize a comprehensive multiset-based game representation and train the policy using a Soft Actor-Critic algorithm adapted to support variable-size sets of actions. The proposed model is the first successful learning-based agent that makes all decisions without relying on heuristics and supports a broader set of game configurations. It exceeds the performance of all previous learning-based approaches and is only outperformed by search-based approaches in certain game configurations. In addition, the paper presents modifications that induce agents to exhibit novel human-like play strategies. Finally, we show that learning strong strategies based on card combinations requires a reinforcement learning algorithm capable of discovering and executing a precise strategy while ignoring simpler suboptimal policies with higher immediate rewards.","PeriodicalId":498041,"journal":{"name":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","volume":"26 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":"135303519","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

Physics-Based Task Generation through Causal Sequence of Physical Interactions 通过物理交互的因果序列生成基于物理的任务

Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Pub Date : 2023-10-06 DOI: 10.1609/aiide.v19i1.27501

Chathura Gamage, Vimukthini Pinto, Matthew Stephenson, Jochen Renz

{"title":"Physics-Based Task Generation through Causal Sequence of Physical Interactions","authors":"Chathura Gamage, Vimukthini Pinto, Matthew Stephenson, Jochen Renz","doi":"10.1609/aiide.v19i1.27501","DOIUrl":"https://doi.org/10.1609/aiide.v19i1.27501","url":null,"abstract":"Performing tasks in a physical environment is a crucial yet challenging problem for AI systems operating in the real world. Physics simulation-based tasks are often employed to facilitate research that addresses this challenge. In this paper, first, we present a systematic approach for defining a physical scenario using a causal sequence of physical interactions between objects. Then, we propose a methodology for generating tasks in a physics-simulating environment using these defined scenarios as inputs. Our approach enables a better understanding of the granular mechanics required for solving physics-based tasks, thereby facilitating accurate evaluation of AI systems' physical reasoning capabilities. We demonstrate our proposed task generation methodology using the physics-based puzzle game Angry Birds and evaluate the generated tasks using a range of metrics, including physical stability, solvability using intended physical interactions, and accidental solvability using unintended solutions. We believe that the tasks generated using our proposed methodology can facilitate a nuanced evaluation of physical reasoning agents, thus paving the way for the development of agents for more sophisticated real-world applications.","PeriodicalId":498041,"journal":{"name":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","volume":"8 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":"135303524","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