A Probabilistic Model of Social Working Memory for Information Retrieval in Social Interactions

A Probabilistic Model of Social Working Memory for Information Retrieval in Social Interactions
Title:
A Probabilistic Model of Social Working Memory for Information Retrieval in Social Interactions
Other Titles:
IEEE Transactions on Cybernetics
DOI:
10.1109/TCYB.2017.2706027
Publication Date:
30 May 2017
Citation:
L. Li, Q. Xu, T. Gan, C. Tan and J. H. Lim, "A Probabilistic Model of Social Working Memory for Information Retrieval in Social Interactions," in IEEE Transactions on Cybernetics, vol. PP, no. 99, pp. 1-13. doi: 10.1109/TCYB.2017.2706027
Abstract:
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.
License type:
PublisherCopyrights
Funding Info:
The work is supported by JCO VIP grant 1335h00098, ASTAR, Singapore.
Description:
(c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
2168-2267
2168-2275
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