A novel DBN-based intention inference algorithm for warship air combat

Linxuan Xu; Dianfeng Qiao; Yan Liang; Linfeng Xu

doi:10.1109/ITAIC49862.2020.9339117

A novel DBN-based intention inference algorithm for warship air combat

Linxuan Xu, Dianfeng Qiao, Yan Liang, Linfeng Xu

School of Automation

Research output: Contribution to journal › Conference article › peer-review

4 Scopus citations

Abstract

It is difficult to determine the dynamic Bayesian network (DBN) parameters applied to the inference of intention in the warship air defense environment. This paper considers time variables based on QMAP algorithm that uses both domain knowledge and a small number of static samples for parameter learning, and then we propose a D-QMAP algorithm suitable for DBN parameter learning, taking into account time variables, which utilizes the timing relationship between samples. The simulation is carried out on the background of warship air defense, and the results are in line with reality, verifying the rationality and effectiveness of the model and algorithm.

Original language	English
Article number	9339117
Pages (from-to)	916-921
Number of pages	6
Journal	ITAIC 2020 - IEEE 9th Joint International Information Technology and Artificial Intelligence Conference
DOIs	https://doi.org/10.1109/ITAIC49862.2020.9339117
State	Published - 2020
Event	9th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2020 - Chongqing, China Duration: 11 Dec 2020 → 13 Dec 2020

Keywords

Dynamic Bayesian network
Intention inference
Parameter learning
Qualitative maximum posterior estimation

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10.1109/ITAIC49862.2020.9339117

Cite this

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title = "A novel DBN-based intention inference algorithm for warship air combat",

abstract = "It is difficult to determine the dynamic Bayesian network (DBN) parameters applied to the inference of intention in the warship air defense environment. This paper considers time variables based on QMAP algorithm that uses both domain knowledge and a small number of static samples for parameter learning, and then we propose a D-QMAP algorithm suitable for DBN parameter learning, taking into account time variables, which utilizes the timing relationship between samples. The simulation is carried out on the background of warship air defense, and the results are in line with reality, verifying the rationality and effectiveness of the model and algorithm.",

keywords = "Dynamic Bayesian network, Intention inference, Parameter learning, Qualitative maximum posterior estimation",

author = "Linxuan Xu and Dianfeng Qiao and Yan Liang and Linfeng Xu",

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year = "2020",

doi = "10.1109/ITAIC49862.2020.9339117",

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TY - JOUR

T1 - A novel DBN-based intention inference algorithm for warship air combat

AU - Xu, Linxuan

AU - Qiao, Dianfeng

AU - Liang, Yan

AU - Xu, Linfeng

PY - 2020

Y1 - 2020

N2 - It is difficult to determine the dynamic Bayesian network (DBN) parameters applied to the inference of intention in the warship air defense environment. This paper considers time variables based on QMAP algorithm that uses both domain knowledge and a small number of static samples for parameter learning, and then we propose a D-QMAP algorithm suitable for DBN parameter learning, taking into account time variables, which utilizes the timing relationship between samples. The simulation is carried out on the background of warship air defense, and the results are in line with reality, verifying the rationality and effectiveness of the model and algorithm.

AB - It is difficult to determine the dynamic Bayesian network (DBN) parameters applied to the inference of intention in the warship air defense environment. This paper considers time variables based on QMAP algorithm that uses both domain knowledge and a small number of static samples for parameter learning, and then we propose a D-QMAP algorithm suitable for DBN parameter learning, taking into account time variables, which utilizes the timing relationship between samples. The simulation is carried out on the background of warship air defense, and the results are in line with reality, verifying the rationality and effectiveness of the model and algorithm.

KW - Dynamic Bayesian network

KW - Intention inference

KW - Parameter learning

KW - Qualitative maximum posterior estimation

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U2 - 10.1109/ITAIC49862.2020.9339117

DO - 10.1109/ITAIC49862.2020.9339117

M3 - 会议文章

AN - SCOPUS:85101475434

SN - 2693-2865

SP - 916

EP - 921

JO - ITAIC 2020 - IEEE 9th Joint International Information Technology and Artificial Intelligence Conference

JF - ITAIC 2020 - IEEE 9th Joint International Information Technology and Artificial Intelligence Conference

M1 - 9339117

T2 - 9th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2020

Y2 - 11 December 2020 through 13 December 2020

ER -

A novel DBN-based intention inference algorithm for warship air combat

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Keywords

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