2018 IEEE International Conference on Agents (ICA)最新文献_第3页

The Competitions of Forgiving Strategies in the Iterated Prisoner's Dilemma 迭代囚徒困境中宽恕策略的竞争

2018 IEEE International Conference on Agents (ICA) Pub Date : 2018-07-01 DOI: 10.1109/AGENTS.2018.8460036

Ruchdee Binmad, Mingchu Li, Nakema Deonauth, Theerawat Hungsapruek, Aree Limwudhikraijirath

引用次数: 0

Inverse Reinforcement Learning Approach for Elicitation of Preferences in Multi-objective Sequential Optimization 多目标序列优化中偏好激发的逆强化学习方法

2018 IEEE International Conference on Agents (ICA) Pub Date : 2018-07-01 DOI: 10.1109/AGENTS.2018.8460075

A. Ikenaga, S. Arai

引用次数: 8

Human Action Recognition System based on Skeleton Data 基于骨骼数据的人体动作识别系统

2018 IEEE International Conference on Agents (ICA) Pub Date : 2018-07-01 DOI: 10.1109/AGENTS.2018.8458495

Tin Zar Wint Cho, May Thu Win, Aung Win

引用次数: 6

Quantitatively Evaluating Difficulty in Reaching Agreements in Multilateral Closed Negotiation Scenarios 定量评估在多边闭门谈判情景下达成协议的难度

2018 IEEE International Conference on Agents (ICA) Pub Date : 2018-07-01 DOI: 10.1109/AGENTS.2018.8460052

Tatsuya Toyama, Takayuki Ito

{"title":"Quantitatively Evaluating Difficulty in Reaching Agreements in Multilateral Closed Negotiation Scenarios","authors":"Tatsuya Toyama, Takayuki Ito","doi":"10.1109/AGENTS.2018.8460052","DOIUrl":"https://doi.org/10.1109/AGENTS.2018.8460052","url":null,"abstract":"Negotiation is one type of these possible interactions through which intelligent agents can resolve their conflicts and maximize their utility. Furthermore, automated negotiation approaches are expected to greatly reduce the efforts that stakeholders have to expend during real-life negotiations. In this regard, we conceal the preference information of negotiation participants to protect privacy in a real-world negotiation environment. However, in such a negotiation environment, it is difficult for negotiation participants to search effective agreement candidates as reaching agreements. Therefore, in this study, we propose a metric called the Metric of Opposition Level (MOL), which is used for analyzing negotiation scenarios in an environment in which participants' preferences are concealed. The proposed metric MOL quantitatively indicates the difficulty in reaching an agreement by measuring how hostile the opponent agent is. In particular, a third person can analyze negotiation scenarios in consideration of the difficulty in negotiation participants searching agreement candidates. Experimental results indicate the impact of the MOL on agent negotiation results and its vital role in building better negotiation strategies.","PeriodicalId":248901,"journal":{"name":"2018 IEEE International Conference on Agents (ICA)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130105383","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

Part III: Crowd Intelligence 第三部分:群体智能

2018 IEEE International Conference on Agents (ICA) Pub Date : 2018-07-01 DOI: 10.1109/agents.2018.8460028

引用次数: 0

Efficient Task Allocation with Communication Delay Based on Reciprocal Teams 基于对等团队的通信延迟高效任务分配

2018 IEEE International Conference on Agents (ICA) Pub Date : 2018-07-01 DOI: 10.1109/AGENTS.2018.8460001

Ryo Funato, T. Sugawara

引用次数: 0

Developing Arbitrage Strategy in High-frequency Pairs Trading with Filterbank CNN Algorithm 用Filterbank CNN算法开发高频对交易套利策略

2018 IEEE International Conference on Agents (ICA) Pub Date : 2018-07-01 DOI: 10.1109/AGENTS.2018.8459920

Yu-Ying Chen, Wei-Lun Chen, Szu-Hao Huang

{"title":"Developing Arbitrage Strategy in High-frequency Pairs Trading with Filterbank CNN Algorithm","authors":"Yu-Ying Chen, Wei-Lun Chen, Szu-Hao Huang","doi":"10.1109/AGENTS.2018.8459920","DOIUrl":"https://doi.org/10.1109/AGENTS.2018.8459920","url":null,"abstract":"Pairs trading is a statistical arbitrage strategy, which selects a set of assets with similar performance and produces profits during these asset prices far away from rational equilibrium. Once this phenomenon exists, traders can earn the spread by longing the underperforming asset and shorting the outperforming asset. This paper proposed a novel intelligent high-frequency pairs trading system in Taiwan Stock Index Futures (TX) and Mini Index Futures (MTX) market based on deep learning techniques. This research utilized the improved time series visualization method to transfer historical volatilities with different time frames into 2D images which are helpful in capturing arbitrage signals. Moreover, this research improved convolutional neural networks (CNN) model by combining the financial domain knowledge and filterbank mechanism. We proposed Filterbank CNN to extract high-quality features by replacing the random-generating filters with the arbitrage knowledge filters. In summary, the accuracy is enhanced through the proposed method, and it proves that the integrated information technology and financial knowledge could create the better pairs trading system.","PeriodicalId":248901,"journal":{"name":"2018 IEEE International Conference on Agents (ICA)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121413347","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: 14

Part IV: Mechanism Design 第四部分:机制设计

2018 IEEE International Conference on Agents (ICA) Pub Date : 2018-07-01 DOI: 10.1109/agents.2018.8460089

Jun Shu

引用次数: 100

Intelligent Dengue Infoveillance Using Gated Recurrent Neural Learning and Cross-Label Frequencies 基于门控递归神经学习和交叉标记频率的登革热智能信息监测

2018 IEEE International Conference on Agents (ICA) Pub Date : 2018-07-01 DOI: 10.1109/AGENTS.2018.8459963

Evan Dennison S. Livelo, C. Cheng

{"title":"Intelligent Dengue Infoveillance Using Gated Recurrent Neural Learning and Cross-Label Frequencies","authors":"Evan Dennison S. Livelo, C. Cheng","doi":"10.1109/AGENTS.2018.8459963","DOIUrl":"https://doi.org/10.1109/AGENTS.2018.8459963","url":null,"abstract":"With dengue becoming a major concern in tropical countries such as the Philippines, it is important that public health officials are able to accurately determine the presence and magnitude of dengue activity as quickly as possible to facilitate fast emergency response. The prevalence of massive streams of publicly available data from social media make this possible through infoveillance. Infoveillance involves observing and analyzing online interactions to gather health-related data for informing decisions on public health. In this paper, we present a public health agent model that performs dengue infoveillance using a gated recurrent neural network classification model incorporated with pre-trained word embeddings and cross-label frequency calculation. We setup the agent to work on the Philippine Twitter stream as its primary environment. Further, we evaluate the agents classification ability using a holdout set of human-labeled tweets. Afterwards, we run a historical simulation where the trained agent works with a stream of six months worth of tweets from the Philippines and we correlate its infoveillance results with actual dengue morbidity data of that time period. Experiments show that the agent is capable of accurately identifying dengue-related tweets with low loss. Moreover, we confirm that the agent model can be used for determining actual dengue activity and can serve as an early warning system with high confidence.","PeriodicalId":248901,"journal":{"name":"2018 IEEE International Conference on Agents (ICA)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126738989","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: 11

Part VI: Social Networks and Social Learning 第六部分:社会网络和社会学习

2018 IEEE International Conference on Agents (ICA) Pub Date : 2018-07-01 DOI: 10.1109/agents.2018.8460077

引用次数: 0