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
通讯作者地址：[Li, LY]Inst Infocomm Res, Dept Visual Comp, Singapore 138632, Singapore.
来源：IEEE TRANSACTIONS ON CYBERNETICS
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.