LB f T: Learning Bayesian Network Structures from Text in Autonomous Typhoon Response Systems

Yinghui Pan, Junhan Chen, Yifeng Zeng, Zhangrui Yao, Qianwen Li, Biyang Ma, Yi Ji, Zhong Ming

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Discovering variables and understanding their relations, which impacts emergency response, provide important knowledge to the development of decision models, e.g. Bayesian networks, in autonomous typhoon response systems (ATRS). Given the text inputs (containing natural language), learning the network structures still remains a challenge although learning Bayesian networks from data has been extensively investigated in various fields. In this demo, we develop a deep learning based framework for identifying typhoon relevant variables and build their causal relations from text. We use the CausalBank dataset and typhoon specific relation rules to refine the learned relations and allow users to further improve the models through their domain knowledge. We integrate the new learning tool into the existing ATRS and demonstrate the empirical results through real-world typhoon reports.

Original languageEnglish
Title of host publicationInternational Conference on Autonomous Agents and Multiagent Systems, AAMAS 2022
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Pages1914-1916
Number of pages3
ISBN (Electronic)9781713854333
Publication statusPublished - 2022
Event21st International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2022 - Auckland, Virtual, New Zealand
Duration: 9 May 202213 May 2022

Publication series

NameProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Volume3
ISSN (Print)1548-8403
ISSN (Electronic)1558-2914

Conference

Conference21st International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2022
Country/TerritoryNew Zealand
CityAuckland, Virtual
Period9/05/2213/05/22

Keywords

  • Autonomous Typhoon Response Systems
  • Decision Models
  • Learning Bayesian Networks

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