Toward Interpretable-AI Policies Using Evolutionary Nonlinear Decision Trees for Discrete-Action Systems-Reference-Cited by-同舟云学术

Toward Interpretable-AI Policies Using Evolutionary Nonlinear Decision Trees for Discrete-Action Systems

Published:2022 Issue: Volume: Page:1-13
ISSN:2168-2267
Container-title:IEEE Transactions on Cybernetics
language:
Short-container-title:IEEE Trans. Cybern.

Author:

Dhebar Yashesh¹^ORCID,Deb Kalyanmoy²^ORCID,Nageshrao Subramanya³,Zhu Ling³,Filev Dimitar³^ORCID

Affiliation:

1. Department of Mechanical Engineering, Michigan State University, East Lansing, MI, USA

2. Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, USA

3. Research and Advanced Engineering, Ford Motor Company, Dearborn, MI, USA

Funder

Ford-MSU Alliance Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Human-Computer Interaction,Information Systems,Control and Systems Engineering,Software

Link

http://xplorestaging.ieee.org/ielx7/6221036/6352949/09805655.pdf?arnumber=9805655

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