An intention understanding algorithm based on multimodal fusion

LuRong Yang; Zhiquan Feng; Qingbei Guo; Jinlan Tian

doi:10.1117/12.2623101

1 December 2021 An intention understanding algorithm based on multimodal fusion

LuRong Yang, Zhiquan Feng, Qingbei Guo, Jinlan Tian

Proceedings Volume 12079, Second IYSF Academic Symposium on Artificial Intelligence and Computer Engineering; 120790O (2021) https://doi.org/10.1117/12.2623101
Event: 2nd IYSF Academic Symposium on Artificial Intelligence and Computer Engineering, 2021, Xi'an, China

Abstract

This paper presents an intention understanding method for intelligent chemical experiment system. The innovations of this method mainly include, (a) Constructing an intention database according to the standardization of chemical experiment operation, (b) Quantifying and fusing the behavior information of different modes, constructing mathematical models, and calculate behavior intention probability of experimenters in real time, and (c) According to the predicted intention, the machine carries out active human-machine cooperation. The method in this paper can effectively obtain rich intention information from the experimenter's operation behavior which will help to establish a natural human-computer interaction environment and realize the active cooperative work between human and machine. The method in this paper has been evaluated and verified in an intelligent chemistry experiment system based on Unity.

Citation Download Citation

LuRong Yang, Zhiquan Feng, Qingbei Guo, and Jinlan Tian "An intention understanding algorithm based on multimodal fusion", Proc. SPIE 12079, Second IYSF Academic Symposium on Artificial Intelligence and Computer Engineering, 120790O (1 December 2021); https://doi.org/10.1117/12.2623101

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