Interpretable Robust Decision Making

被引：0

作者：

Cooper, Hal James ^{[1
]}

Iyengar, Garud ^{[1
]}

Lin, Ching-Yung ^{[2
]}

机构：

[1] Columbia Univ, New York, NY 10032 USA

[2] Graphen Inc, New York, NY USA

来源：

PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS (AAMAS' 18) | 2018年

关键词：

Interpretable agent modelling; deep learning; robustness;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Interpretable decision making frameworks allow us to easily endow agents with specific goals, risk tolerances, and understanding. Existing decision making systems either forgo interpretability, or pay for it with severely reduced efficiency and large memory requirements. In this paper, we outline DeepID, a neural network approximation of Influence Diagrams, that avoids both pitfalls.

引用

页码：1912 / 1914

页数：3

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