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Hierarchical control models for multimodal process modeling.

Weidong Zhang¹, Feng Chen, Wenli Xu

¹Department of Automation, Tsinghua University, Beijing, China. zwd03@mails.tsinghua.edu.cn

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|April 4, 2009

Summary

This summary is machine-generated.

We introduce a hierarchical control model (HCM) for complex systems. This model effectively manages multimodal processing and system evolution, outperforming existing methods in activity recognition tasks.

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Area of Science:

Computer Science
Artificial Intelligence
Systems Engineering

Background:

Multimodal and hierarchical systems present significant challenges for process modeling.
Existing models struggle to capture the complex interplay between different system components and their evolution over time.

Purpose of the Study:

To propose a novel Hierarchical Control Model (HCM) for effectively modeling and controlling hierarchically multimodal systems.
To develop a new information criterion for accurate state order estimation in complex models.

Main Methods:

Developed a hierarchical control model (HCM) utilizing a control layer to extract group processes and manage modality evolution.
Represented system hierarchy using a multilayer network.
Introduced a new information criterion for state order estimation, correcting for model complexity bias.

Main Results:

The proposed HCM successfully models influences within hierarchical multimodal systems.
The new information criterion demonstrates improved accuracy in state order estimation.
Comparisons on multiagent activity recognition tasks show HCMs are reliable and efficient.

Conclusions:

HCM provides a robust framework for process modeling in complex, hierarchically multimodal systems.
The developed information criterion enhances model selection for such systems.
HCM offers a promising approach for applications like multiagent activity recognition.