TY - GEN
T1 - LB f T
T2 - 21st International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2022
AU - Pan, Yinghui
AU - Chen, Junhan
AU - Zeng, Yifeng
AU - Yao, Zhangrui
AU - Li, Qianwen
AU - Ma, Biyang
AU - Ji, Yi
AU - Ming, Zhong
N1 - 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).
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Autonomous Typhoon Response Systems
KW - Decision Models
KW - Learning Bayesian Networks
UR - http://www.scopus.com/inward/record.url?scp=85134322934&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85134322934
T3 - Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
SP - 1914
EP - 1916
BT - International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2022
PB - International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Y2 - 9 May 2022 through 13 May 2022
ER -