Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment最新文献_第5页

Reconstructing Existing Levels through Level Inpainting 通过关卡绘制重建现有关卡

Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Pub Date : 2023-10-06 DOI: 10.1609/aiide.v19i1.27523

Johor Jara Gonzalez, Matthew Guzdial

引用次数: 0

Distribution Fairness in Multiplayer AI Using Shapley Constraints 基于Shapley约束的多人游戏AI分配公平性

Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Pub Date : 2023-10-06 DOI: 10.1609/aiide.v19i1.27519

Robert C. Gray, Jichen Zhu, Santiago Ontañón

{"title":"Distribution Fairness in Multiplayer AI Using Shapley Constraints","authors":"Robert C. Gray, Jichen Zhu, Santiago Ontañón","doi":"10.1609/aiide.v19i1.27519","DOIUrl":"https://doi.org/10.1609/aiide.v19i1.27519","url":null,"abstract":"Experience management (EM) agents in multiplayer serious games face unique challenges and responsibilities regarding the fair treatment of players. One such challenge is the Greedy Bandit Problem that arises when using traditional Multi-Armed Bandits (MABs) as EM agents, which results in some players routinely prioritized while others may be ignored. We will show that this problem can be a cause of player non-adherence in a multiplayer serious game played by human users. To mitigate this effect, we propose a new bandit strategy, the Shapley Bandit, which enforces fairness constraints in its treatment of players based on the Shapley Value. We evaluate our approach via simulation with virtual players, finding that the Shapley Bandit can be effective in providing more uniform treatment of players while incurring only a slight cost in overall performance to a typical greedy approach. Our findings highlight the importance of fair treatment among players as a goal of multiplayer EM agents and discuss how addressing this issue may lead to more effective agent operation overall. The study contributes to the understanding of player modeling and EM in serious games and provides a promising approach for balancing fairness and engagement in multiplayer environments.","PeriodicalId":498041,"journal":{"name":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","volume":"36 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":"135303342","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

Persona Agents from Playtraces and Emotion 来自Playtraces和Emotion的角色代理

Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Pub Date : 2023-10-06 DOI: 10.1609/aiide.v19i1.27517

Pedro M. Fernandes, Manuel Lopes, Rui Prada

引用次数: 0

The Five-Dollar Model: Generating Game Maps and Sprites from Sentence Embeddings 5美元模型:基于句子嵌入生成游戏地图和精灵

Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Pub Date : 2023-10-06 DOI: 10.1609/aiide.v19i1.27506

Timothy Merino, Roman Negri, Dipika Rajesh, M Charity, Julian Togelius

引用次数: 0

Entropy as a Measure of Puzzle Difficulty 熵是谜题难度的衡量标准

Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Pub Date : 2023-10-06 DOI: 10.1609/aiide.v19i1.27499

Eugene You Chen Chen, Adam White, Nathan R. Sturtevant

{"title":"Entropy as a Measure of Puzzle Difficulty","authors":"Eugene You Chen Chen, Adam White, Nathan R. Sturtevant","doi":"10.1609/aiide.v19i1.27499","DOIUrl":"https://doi.org/10.1609/aiide.v19i1.27499","url":null,"abstract":"Evaluating and ranking the difficulty and enjoyment of puzzles is important in game design. Typically, such rankings are constructed manually for each specific game, which can be time consuming, subject to designer bias, and requires extensive play testing. An approach to ranking that generalizes across multiple puzzle games is even more challenging because of their variation in factors like rules and goals. This paper introduces two general approaches to compute puzzle entropy, and uses them to evaluate puzzles that players enjoy. The resulting uncertainty score is equivalent to the number of bits of data necessary to communicate the solution of a puzzle to a player of a given skill level. We apply our new approaches to puzzles from the 2016 game, The Witness. The computed entropy scores largely reproduce the order of a set of puzzles that introduce a new mechanic in the game. The scores are also positively correlated with the user ratings of user-created Witness puzzles, providing evidence that our approach captures notions of puzzle difficulty and enjoyment. Our approach is designed to exploit game-specific knowledge in a general way and thus can extended to provide automatic rankings or curricula in a variety of applications.","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":"135303338","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

DendryScope: Narrative Designer Support via Symbolic Analysis DendryScope:通过符号分析支持叙事设计师

Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Pub Date : 2023-10-06 DOI: 10.1609/aiide.v19i1.27527

Jasmine Otto, Autumn Chen, Adam M. Smith

引用次数: 0