标题:A Probabilistic Model of Social Working Memory for Information Retrieval in Social Interactions
作者:Li, Liyuan; Xu, Qianli; Gan, Tian; Tan, Cheston; Lim, Joo-Hwee
作者机构:[Li, Liyuan; Xu, Qianli; Tan, Cheston; Lim, Joo-Hwee] Inst Infocomm Res, Dept Visual Comp, Singapore 138632, Singapore.; [Gan, Tian] Shandong Univ, 更多
通讯作者:Li, Liyuan
通讯作者地址:[Li, LY]Inst Infocomm Res, Dept Visual Comp, Singapore 138632, Singapore.
来源:IEEE TRANSACTIONS ON CYBERNETICS
出版年:2018
卷:48
期:5
页码:1540-1552
DOI:10.1109/TCYB.2017.2706027
关键词:Artificial social intelligence (ASI); Bayesian cognitive model (BCM);; cognitive modeling; computational social intelligence; generative model;; machine learning; personality model; probabilistic model; social working; memory (SWM); statistical learning
摘要:Social working memory (SWM) plays an important role in navigating social interactions. Inspired by studies in psychology, neuroscience, cognitive science, and machine learning, we propose a probabilistic model of SWM to mimic human social intelligence for personal information retrieval (IR) in social interactions. First, we establish a semantic hierarchy as social long-term memory to encode personal information. Next, we propose a semantic Bayesian network as the SWM, which integrates the cognitive functions of accessibility and self-regulation. One subgraphical model implements the accessibility function to learn the social consensus about IR-based on social information concept, clustering, social context, and similarity between persons. Beyond accessibility, one more layer is added to simulate the function of self-regulation to perform the personal adaptation to the consensus based on human personality. Two learning algorithms are proposed to train the probabilistic SWM model on a raw dataset of high uncertainty and incompleteness. One is an efficient learning algorithm of Newton's method, and the other is a genetic algorithm. Systematic evaluations show that the proposed SWM model is able to learn human social intelligence effectively and outperforms the baseline Bayesian cognitive model. Toward real-world applications, we implement our model on Google Glass as a wearable assistant for social interaction.
收录类别:EI;SCOPUS;SCIE;SSCI
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045079252&doi=10.1109%2fTCYB.2017.2706027&partnerID=40&md5=320c27d32aa721a952304433afb32c93
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