The International FLAIRS Conference Proceedings最新文献_第7页

Semantics-based Framework for Incentivized Research Data Sharing 基于语义的激励研究数据共享框架

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133374

Kacy Adams, D. McGuinness, O. Seneviratne

引用次数: 0

Towards a multi-modal Deep Learning Architecture for User Modeling 面向用户建模的多模态深度学习架构

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133328

A. Tato, R. Nkambou

{"title":"Towards a multi-modal Deep Learning Architecture for User Modeling","authors":"A. Tato, R. Nkambou","doi":"10.32473/flairs.36.133328","DOIUrl":"https://doi.org/10.32473/flairs.36.133328","url":null,"abstract":"Deep learning has succeeded in various applications, including image classification and feature learning. However, there needs to be more research on its use in Intelligent Tutoring Systems or Serious Games, particularly in modeling user behavior during learning or gaming sessions using multi-modal data. Creating an effective user model is crucial for developing a highly adaptive system. To achieve this, it is necessary to consider all available data sources to inform the user’s current state. This study proposes a user-sensitive deep multi-modal architecture that leverages deep learning and user data to extract a rich latent representation of the user. The architecture combines a Long Short-Term Memory, a Convolutional Neural Network, and multiple Deep Neu-ral Networks to handle the multi-modality of data. The resulting model was evaluated on a public multi-modal dataset, achieving better results than state-of-the-art algorithms for a similar task: opinion polarity detection. These findings suggest that the latent representation learned from the data is useful in discriminating behaviors. This proposed solution can be applied in various contexts where user modeling using multi-modal data is critical for improving the user experience.","PeriodicalId":302103,"journal":{"name":"The International FLAIRS Conference Proceedings","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130840491","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

A Survey of Unsupervised Learning Algorithms for Zero-Day Attacks in Intrusion Detection Systems 入侵检测系统中零日攻击的无监督学习算法研究

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133182

Sunkanmi Oluwadare, Zag ElSayed

引用次数: 0

A Project to Compose a Modular AI Certification System in University Edu-cation and its inherent chance to verify, validate, and refine AI teaching by AI technologies 构建大学教育模块化人工智能认证体系项目及其利用人工智能技术验证、验证和完善人工智能教学的内在机会

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133231

Tanja Schramm, R. Knauf

引用次数: 0

On the Comparison of Markov Chains-based Models in Process Mining for Healthcare: A Case Study 基于马尔可夫链的医疗流程挖掘模型比较研究——以实例为例

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133049

M. Vallati, S. Orini, Mariagrazia Lorusso, Mariachiara Savino, R. Gatta, M. Filosto

引用次数: 0

Model Representation Considerations for Artificial Intelligence Opponent Development in Combat Games 格斗游戏中人工智能对手开发的模型表示考虑

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133571

Sarah Kitchen, Christopher McGroarty, Timothy Aris

{"title":"Model Representation Considerations for Artificial Intelligence Opponent Development in Combat Games","authors":"Sarah Kitchen, Christopher McGroarty, Timothy Aris","doi":"10.32473/flairs.36.133571","DOIUrl":"https://doi.org/10.32473/flairs.36.133571","url":null,"abstract":"The performance and behavior of an Artificial Intelligence (AI) opponent in games requires coordination of multiple agents and complex tasks depends on many design choices made during implementation. Currently, gaming agents developed with Reinforcement Learning (RL) methods are constructed to play the game, leading to natural design choices for observations, actions, and rewards that are congruent with a human player's actions and objectives. However, in simulation and serious games, the objective of the implemented opponent should be developed in a way that supports the learning objectives for the user, such as by including additional ground truth environment data in the observation space or action structure. Therefore, the reward structure for the AI needs to incorporate more sophisticated considerations than just whether the game was won or lost by the AI. In this way the design space for opponent AI in these settings is considerably broader than what is traditionally used for RL gaming AI. This paper considers the implications of observation representation and reward design for the AI agent and associated actions in the context of 2-player battlefield-type games that are not strictly zero-sum. Semi-cooperative and fully competitive models are considered. The environment in these games is a spatially extended battlefield in which agents must maneuver their forces to bring them into combat range of each other. The objective of the game is control over a pre-specified location in the game, and combat is executed via Lanchester attrition. We demonstrate the impact of aggregation on stochasticity of the model, where aggregation of the state model is controlled by various entropy-based metrics, as well as on the policy learned by an RL agent. Generalizations to alternative scenarios and objectives are discussed, as well as applications to the development of an AI combat opponent that can cohesively manage its forces over multiple scales.","PeriodicalId":302103,"journal":{"name":"The International FLAIRS Conference Proceedings","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123888786","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

Relative Performance of Bilateral Multiattribute Negotiation Strategies in Open Markets 开放市场中双边多属性谈判策略的相对绩效

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133362

Jacob Brue, Joseph Shymanski, Selim Karaoglu, S. Sen

引用次数: 0

DanfeNER - Named Entity Recognition in Nepali Tweets DanfeNER——尼泊尔语推文中的命名实体识别

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133384

Nobal B. Niraula, Jeevan Chapagain

引用次数: 0

Leveraging Graph Networks to Model Environments in Reinforcement Learning 利用图网络在强化学习中对环境建模

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133118

Viswanath Chadalapaka, Volkan Ustun, Lixing Liu

{"title":"Leveraging Graph Networks to Model Environments in Reinforcement Learning","authors":"Viswanath Chadalapaka, Volkan Ustun, Lixing Liu","doi":"10.32473/flairs.36.133118","DOIUrl":"https://doi.org/10.32473/flairs.36.133118","url":null,"abstract":"This paper proposes leveraging graph neural networks (GNNs) to model an agent’s environment to construct superior policy networks in reinforcement learning (RL). To this end, we explore the effects of different combinations of GNNs and graph network pooling functions on policy performance. We also run experiments at different levels of problem complexity, which affect how easily we expect an agent to learn an optimal policy and therefore show whether or not graph networks are effective at various problem complexity levels. The efficacy of our approach is shown via experimentation in a partially-observable, non-stationary environment that parallels the highly-practical scenario of a military training exercise with human trainees, where the learning goal is to become the best sparring partner possible for human trainees. Our results present that our models can generate better-performing sparring partners by employing GNNs, as demonstrated by these experiments in the proof-of-concept environment. We also explore our model’s applicability in Multi-Agent RL scenarios. Our code is available online at https://github.com/Derposoft/GNNsAsEnvs.","PeriodicalId":302103,"journal":{"name":"The International FLAIRS Conference Proceedings","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129880338","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

Visualization of Learning Process in Feature Space 特征空间中学习过程的可视化

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133329

Tomohiro Inoue, Noboru Murata, Taiki Sugiura

引用次数: 0