A Novel Continuous Learning and Collaborative Decision Making Mechanism for Real-Time Cooperation of Humanoid Service Robots

Ming Jiang, Li Zhang

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

2 Citations (Scopus)

Abstract

This paper introduces and proposes a novel Continuous Learning and Collaborative Decision Making (CLCDM) mechanism to support the real-time cooperation of affective humanoid service robots in smart home/campus environment, in which many highly complicated and intelligence demanding applications, such as homecare and children education are either currently partly assisted or expected to be fully provided in the future by the collaborations of intelligent and affective humanoid robots. The core of the CLCDM approach is a streaming data analytics framework, which incorporates Big Data Analytics facilities and decision making under uncertainty techniques to facilitate the provision of CLCDM capability for affective humanoid service robots to succeed in serving human users needs. An experimental case study is conducted to validate a prototype implementation of the CLCDM approach and the preliminary result demonstrates the feasibility and effectiveness of the promising approach.
Original languageEnglish
Title of host publication2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages221-225
ISBN (Print)978-1-5090-0153-8
DOIs
Publication statusPublished - 26 Oct 2015

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