2022 IEEE International Conference on Agents (ICA)最新文献

Intelligent Agents in Educational Institutions: NEdBOT - NLP-based Chatbot for Administrative Support Using DialogFlow 教育机构中的智能代理:使用DialogFlow的基于nlp的行政支持聊天机器人NEdBOT

2022 IEEE International Conference on Agents (ICA) Pub Date : 2022-11-01 DOI: 10.1109/ICA55837.2022.00012

Muhammad Shahroze Ali, F. Azam, Aon Safdar, Muhammad Waseem Anwar

{"title":"Intelligent Agents in Educational Institutions: NEdBOT - NLP-based Chatbot for Administrative Support Using DialogFlow","authors":"Muhammad Shahroze Ali, F. Azam, Aon Safdar, Muhammad Waseem Anwar","doi":"10.1109/ICA55837.2022.00012","DOIUrl":"https://doi.org/10.1109/ICA55837.2022.00012","url":null,"abstract":"Artificial intelligence (AI)-based chatbot systems have seen increased adaption in the educational domain in recent years owing to increased sophistication in the AI domain. However, most of the communication between students and educational institutions is still performed physically and causes major administrative overhead, especially during the time of admission. Contemporary pattern-matching-based and generative-based chatbots underperform to queries outside a limited scope, grammatically and structurally ambiguous inputs, outliers to pre-defined rule-set, and longer response times for a huge knowledge base. We proposed a NEdBOT-An NLP-based Educational Bot, developed by Natural Language Processing models integrated within the DialogFlow platform utilizing a Retrieval-based approach. We evaluate the developed chatbot on a custom dataset generated for the admissions use case of a prominent university. We used an objective evaluation criterion with real-world users to achieve an intent classification accuracy of 76.8% at an average mean response time of 216.43ms per query and a user-friendliness score of 72% on the System Usability Scale (SUS). The results demonstrate the proposed approach's ability to create robust, reliable, responsive, and user-friendly web-based smart chatbots that are highly scalable with the capability to handle wider scopes and vague inputs with ease.","PeriodicalId":150818,"journal":{"name":"2022 IEEE International Conference on Agents (ICA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122155948","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

Trajectory Planning for A Massive Number of UAVs in the Environment with Static and Dynamic Obstacles: A Mean Field Game Approach 静态和动态障碍环境下大量无人机的轨迹规划:平均场博弈方法

2022 IEEE International Conference on Agents (ICA) Pub Date : 2022-11-01 DOI: 10.1109/ICA55837.2022.00016

Zijia Niu, Yuxin Jin, Wang Yao, Xiao Zhang, Lu Ren

引用次数: 0

Quantitative Tuning of Artificial Market Simulation using Generative Adversarial Network 基于生成对抗网络的人工市场模拟定量调整

2022 IEEE International Conference on Agents (ICA) Pub Date : 2022-11-01 DOI: 10.1109/ICA55837.2022.00009

Masanori Hirano, K. Izumi

{"title":"Quantitative Tuning of Artificial Market Simulation using Generative Adversarial Network","authors":"Masanori Hirano, K. Izumi","doi":"10.1109/ICA55837.2022.00009","DOIUrl":"https://doi.org/10.1109/ICA55837.2022.00009","url":null,"abstract":"This study focuses on parameter tuning of artificial market simulations and aims to replace the traditional qualitative evaluation metrics based on stylized facts with the proposed quantitative metrics. Traditionally, for the evaluation of artificial market simulations, the replication of stylized facts, a common phenomenon among financial markets and observed in empirical studies, is verified by humans. However, this prevents large-scale parameter tuning owing to the complexity of automation. Hence, this study utilizes a generative adversarial network (GAN) for this replacement because we assume that the GAN's learning architecture has a good fit for evaluating the distributional features of actual markets and can learn stylized facts implicitly. In the proposed parameter-tuning method, the simulated data are input into the critic of the GAN, and the outputs are employed as the objective value of the tuning. The parameter tuning results show that we successfully tuned the high-dimensional parameters of artificial market simulations and confirmed that the optimized parameter could replicate the stylized facts employed in traditional qualitative evaluation metrics.","PeriodicalId":150818,"journal":{"name":"2022 IEEE International Conference on Agents (ICA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132782479","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: 1

Justice League: Time-series Game Player Pattern Detection to Discover Rank-Skill Mismatch 正义联盟:时间序列游戏玩家模式检测发现等级技能不匹配

2022 IEEE International Conference on Agents (ICA) Pub Date : 2022-11-01 DOI: 10.1109/ICA55837.2022.00014

Hae-Na Kim, Sangho Lee, Jiyoung Woo, H. Kim

{"title":"Justice League: Time-series Game Player Pattern Detection to Discover Rank-Skill Mismatch","authors":"Hae-Na Kim, Sangho Lee, Jiyoung Woo, H. Kim","doi":"10.1109/ICA55837.2022.00014","DOIUrl":"https://doi.org/10.1109/ICA55837.2022.00014","url":null,"abstract":"When rank and skill do not coincide in competitive games, this might be a sign of issues such as boosting, smurfing, and trolling occur. The fair gaming culture of online gaming is disrupted and offended by cheating like boosting, smurfing, and trolling. The player's play style must be used to determine the rank that appears in account information. In this study, we classified League of Legends' low and high tiers using a sequence-based CNN-LSTM model. Using input perturbation, the model can explain its own importance for certain features. The experimental progress: First, we selected features that show a difference between tiers by an extracted score estimating a cumulative sum graph. Second, we construct the dataset format variously with variable or fixed sequence length, compare performance, and analyze the pros and cons. Finally, we consider the possibility of early detection by measuring performance over game elapsed time. Along with the experiment, a rank classification performance of the model achieved AUC 0.9036 and found that we can distinguish from the 24 minutes after the start of the game. In addition, We derived that ccReduction and MinionsKilied were the information that had the most influence on skills among various features.","PeriodicalId":150818,"journal":{"name":"2022 IEEE International Conference on Agents (ICA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133156982","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

GORITE: A BDI Realisation of Behavior Trees GORITE:行为树的BDI实现

2022 IEEE International Conference on Agents (ICA) Pub Date : 2022-11-01 DOI: 10.1109/ICA55837.2022.00008

Lui Cirocco, D. Jarvis, J. Jarvis, R. Rönnquist

{"title":"GORITE: A BDI Realisation of Behavior Trees","authors":"Lui Cirocco, D. Jarvis, J. Jarvis, R. Rönnquist","doi":"10.1109/ICA55837.2022.00008","DOIUrl":"https://doi.org/10.1109/ICA55837.2022.00008","url":null,"abstract":"Behavior trees are becoming increasingly used in both the computer games and robotics industries as a mechanism for non-programmers to specify individual entity (non-playing character or robot) behaviors. The tree representation that is employed is amenable to graphical representation and the availability of graphical editors has contributed significantly to the uptake of the approach. Working with graphical representations of behavior makes development by non-programmers easier. However, existing behavior tree representations are limited in their ability to represent team behavior and complex entity reasoning. Agent frameworks exist that address these issues and while they have been successfully employed in military war gaming applications, the underlying behavior representation that they use are fundamentally different to behavior trees. In this paper, we introduce GORITE, a BDI agent framework in which agent behavior is specified using goal-based process models which are representationally similar to behavior trees. Unlike behavior trees, process models can be used to represent both individual and team behaviors. Furthermore, GORITE provides support to enable an agent (or team of agents) to reason about the goals that it intends to pursue as well as the goals that it is currently pursuing. A simple example is used to demonstrate how team behavior can be modelled using GORITE process models.","PeriodicalId":150818,"journal":{"name":"2022 IEEE International Conference on Agents (ICA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113998869","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 Percolation-Based Secure Routing Protocol for Wireless Sensor Networks 基于渗透的无线传感器网络安全路由协议

2022 IEEE International Conference on Agents (ICA) Pub Date : 2022-11-01 DOI: 10.1109/ICA55837.2022.00017

Jie Jiang, P. Long, Lijia Xie, Z. Zheng

{"title":"A Percolation-Based Secure Routing Protocol for Wireless Sensor Networks","authors":"Jie Jiang, P. Long, Lijia Xie, Z. Zheng","doi":"10.1109/ICA55837.2022.00017","DOIUrl":"https://doi.org/10.1109/ICA55837.2022.00017","url":null,"abstract":"Wireless Sensor Networks (WSN) have assisted applications of multi-agent system. Abundant sensor nodes, densely distributed around a base station (BS), collect data and transmit to BS node for data analysis. The concept of cluster has been emerged as the efficient communication structure in resource-constrained environment. However, the security still remains a major concern due to the vulnerability of sensor nodes. In this paper, we propose a percolation-based secure routing protocol. We leverage the trust score composed of three indexes to select cluster heads (CH) for unevenly distributed clusters. By considering the reliability, centrality and stability, legitimate nodes with social trust and adequate energy are chosen to provide relay service. Moreover, we design a multi-path inter-cluster routing protocol to construct CH chains for directed inter-cluster data transmission based on the percolation. And the measurement of transit score for on-path CH nodes contributes to load balancing and security. Our simulation results show that our protocol is able to guarantee the security to improve the delivery ratio and packets delay.","PeriodicalId":150818,"journal":{"name":"2022 IEEE International Conference on Agents (ICA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125557206","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

The Relationship Between Agent-based Simulation and Game Theory in the Case of Parallel Trade 并行交易中基于agent的仿真与博弈论的关系

2022 IEEE International Conference on Agents (ICA) Pub Date : 2022-11-01 DOI: 10.1109/ICA55837.2022.00013

Ruhollah Jamali, S. Lazarova-Molnar

{"title":"The Relationship Between Agent-based Simulation and Game Theory in the Case of Parallel Trade","authors":"Ruhollah Jamali, S. Lazarova-Molnar","doi":"10.1109/ICA55837.2022.00013","DOIUrl":"https://doi.org/10.1109/ICA55837.2022.00013","url":null,"abstract":"Pharmaceutical parallel trade emerged due to the European Union's single market for medicines. While many players, such as manufacturers, wholesalers, parallel traders, pharmacies, regulatory authorities, and hospitals, are involved in this market, having a model that accurately reflects the parallel trade market could be a considerable advantage for players in this market. One way to model the parallel trade market is by employing game theory, which is frequently used to model and explain business interactions. However, game theory imposes limitations on models. Agent-based modeling is a promising framework for studying the parallel trade market, which allows us to investigate macroscopic outcomes that emerge from microscopic rules, decisions, and interactions. Moreover, agent-based modeling allows for high expressiveness and complexity in agents, improving agents' efficiency in autonomy and reactivity compared to current game theoretic models. In this paper, we develop the concept of an agent-based model for the pharmaceutical parallel trading market based on an available game-theoretic model of the market.","PeriodicalId":150818,"journal":{"name":"2022 IEEE International Conference on Agents (ICA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128441485","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

Application of Machine Learning in Lifestyle: Weight-In Image Classification using Convolutional Neural Networks 机器学习在生活方式中的应用:卷积神经网络的加权图像分类

2022 IEEE International Conference on Agents (ICA) Pub Date : 2022-11-01 DOI: 10.1109/ICA55837.2022.00018

Warisara Asawaponwiput, Panyawut Sriiesaranusorn, Thawat Mohchit, N. Thatphithakkul, D. Surangsrirat

{"title":"Application of Machine Learning in Lifestyle: Weight-In Image Classification using Convolutional Neural Networks","authors":"Warisara Asawaponwiput, Panyawut Sriiesaranusorn, Thawat Mohchit, N. Thatphithakkul, D. Surangsrirat","doi":"10.1109/ICA55837.2022.00018","DOIUrl":"https://doi.org/10.1109/ICA55837.2022.00018","url":null,"abstract":"Nowadays, people are increasingly concerned for their health as being healthy is regarded as a profitable investment. Obesity is one of the most common health problems that leads to multiple diseases. We work with the team that developed a mobile application to encourage users to change their eating and activity behaviors to improve their health based on a virtual competition platform. Participants are required to upload a weight-in photo to verify their weight before the challenge. Manually verifying these images can be time-consuming and error-prone due to the large number of images in each competition. In this study, we proposed an image classification approach to help screen incorrect images of the weight-in photo for the virtual competition. The image augmentation techniques were applied to the training images before being input into the classification model. Since the goal is to deploy the model in a mobile application, the suitable model must be small and efficient enough for use in a limited resources environment. Therefore, VGGNet-16 and MobileNet-V2 were selected as the classification models. The experimental results show that the model could learn from the preprocessed images and obtain satisfactory classification results from pre-trained VGGNet-16 with the highest accuracy and F1-score of 95.00% and 95.23%, respectively. MobileNet-V2 inference time was approximately 10 times faster but the performance was lower with the highest accuracy and F1-score of 93.00% and 93.32%, respectively.","PeriodicalId":150818,"journal":{"name":"2022 IEEE International Conference on Agents (ICA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129848335","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

Prototyping Agents for Resolving Opinion Biases Toward Facilitating Sublation of Conflict in Web-based Discussions 解决意见偏见的原型代理，以促进网络讨论中冲突的消除

2022 IEEE International Conference on Agents (ICA) Pub Date : 2022-11-01 DOI: 10.1109/ICA55837.2022.00010

Hikaru Ishizuka, Shun Shiramatsu, Keiko Ono

引用次数: 0

Agent for Recommending Information Relevant to Web-based Discussion by Generating Query Terms using GPT-3 通过使用GPT-3生成查询词来推荐与基于web的讨论相关的信息的代理

2022 IEEE International Conference on Agents (ICA) Pub Date : 2022-11-01 DOI: 10.1109/ICA55837.2022.00011

Ryosuke Kinoshita, Shun Shiramatsu

{"title":"Agent for Recommending Information Relevant to Web-based Discussion by Generating Query Terms using GPT-3","authors":"Ryosuke Kinoshita, Shun Shiramatsu","doi":"10.1109/ICA55837.2022.00011","DOIUrl":"https://doi.org/10.1109/ICA55837.2022.00011","url":null,"abstract":"In Web discussions, which have become mainstream with COVID-19, the amount of information possessed and the level of understanding of the discussion differ among participants. As a result, some participants may not be able to speak up satisfactorily, and this can hinder consensus building in the discussion as a whole. Therefore, we develop an agent that automatically recommends information related to the discussion as information that facilitates participants to speak up. The agent first obtains necessary discussion data from on-going Web discussions. The information to be recommended is determined by real-time search. Query words for the search are generated using a pre-trained query-term-generation model. When selecting information to recommend from the information obtained in the search, a model that classifies the acquired information according to the discussion phase is used. The results of a discussion experiment in which an agent intervened in a Web-based discussion showed many results indicating the effectiveness of the agent, although there are some points that need to be improved. However, since the scale of the discussion experiment was small, it will be necessary to validate the agent in large-scale discussions in the future.","PeriodicalId":150818,"journal":{"name":"2022 IEEE International Conference on Agents (ICA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121557158","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: 1