Abductive Causal Reasoning for Internet of Things

被引：3

作者：

Raglin, Adrienne ^{[1
]}

Venkateswaran, Anshuman ^{[2
]}

Liu, Huan ^{[2
]}

机构：

[1] US Army Res Lab, Informat Sci Div, Adelphi, MD 20783 USA

[2] Arizona State Univ, Sch Comp Informat & Decis Syst Engn, Tempe, AZ USA

来源：

2019 IEEE SMARTWORLD, UBIQUITOUS INTELLIGENCE & COMPUTING, ADVANCED & TRUSTED COMPUTING, SCALABLE COMPUTING & COMMUNICATIONS, CLOUD & BIG DATA COMPUTING, INTERNET OF PEOPLE AND SMART CITY INNOVATION (SMARTWORLD/SCALCOM/UIC/ATC/CBDCOM/IOP/SCI 2019) | 2019年

关键词：

Abductive Causal Reasoning; IoT; Decision Making;

D O I：

10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00328

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The Internet of Things (IoT) is defined as a "global infrastructure for the information society, enabling advanced services by interconnecting (physical and virtual) things based on existing and evolving interoperable information and communication technologies [1]." This network of heterogeneous devices that are connected can interact and share data as well as aid in decision making. Abductive causal reasoning (ACR) offers a new perspective to learn from data with the capability to support decisions by explaining the context or environment in which the choices are made. In this paper, we propose to use ACR in an IoT environment to infer sensible hypotheses from IoT data and explain how decisions can be made or improved. Examples are given to illustrate how ACR can support decisions from the available data.

引用

页码：1866 / 1869

页数：4

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