@inproceedings{feda2be833b442388685a0ae8f5ad353,
title = "LB f T: Learning Bayesian Network Structures from Text in Autonomous Typhoon Response Systems",
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.",
keywords = "Autonomous Typhoon Response Systems, Decision Models, Learning Bayesian Networks",
author = "Yinghui Pan and Junhan Chen and Yifeng Zeng and Zhangrui Yao and Qianwen Li and Biyang Ma and Yi Ji and Zhong Ming",
note = "Funding Information: Professor Yifeng Zeng received the support from the EPSRC New Investigator Award (Grant No. EP/S011609/1). This work is supported in part by NSFC (Grants No.61772442, 61836005 and 62176225).; 21st International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2022 ; Conference date: 09-05-2022 Through 13-05-2022",
year = "2022",
language = "English",
series = "Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS",
publisher = "International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)",
pages = "1914--1916",
booktitle = "International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2022",
address = "United States",
}