MCAEM: mixed-correlation analysis-based episodic memory for companion–user interactions

MCAEM: mixed-correlation analysis-based episodic memory for companion–user interactions This paper considers episodic memory for companion–human interaction, aiming at improving user experience of interactions by endowing social companions with awareness of past experience. Due to noise and incomplete cues from natural language and speech in real-world interaction, accurate memory retrieval is very challenging and the noise resistance is important in practice. To improve the robustness of companion–human interaction, we propose a mixed-correlation analysis-based episodic memory (MCAEM) model, in which the correlations between memory elements are analyzed and then utilized for memory retrieval. In particular, the correlations are analyzed in three aspects: the relations between elements, importance of attributes and order of events. Based on the mixed-correlation analysis, a new similarity measure is constructed, which has substantially enhanced the noise resistance of memory retrieval. Experiments on a dataset collected from interaction in movies quantitatively evaluate the MCAEM model and compare it with prior work. Also, a user study is conducted to investigate the benefits of integrating the MCAEM model into social companions. The results demonstrate that the companions equipped with the MCAEM model not only have better retrieval performance, but also improve user experience in many aspects. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Visual Computer Springer Journals

MCAEM: mixed-correlation analysis-based episodic memory for companion–user interactions

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer-Verlag GmbH Germany, part of Springer Nature
Subject
Computer Science; Computer Graphics; Computer Science, general; Artificial Intelligence (incl. Robotics); Image Processing and Computer Vision
ISSN
0178-2789
eISSN
1432-2315
D.O.I.
10.1007/s00371-018-1537-3
Publisher site
See Article on Publisher Site

Abstract

This paper considers episodic memory for companion–human interaction, aiming at improving user experience of interactions by endowing social companions with awareness of past experience. Due to noise and incomplete cues from natural language and speech in real-world interaction, accurate memory retrieval is very challenging and the noise resistance is important in practice. To improve the robustness of companion–human interaction, we propose a mixed-correlation analysis-based episodic memory (MCAEM) model, in which the correlations between memory elements are analyzed and then utilized for memory retrieval. In particular, the correlations are analyzed in three aspects: the relations between elements, importance of attributes and order of events. Based on the mixed-correlation analysis, a new similarity measure is constructed, which has substantially enhanced the noise resistance of memory retrieval. Experiments on a dataset collected from interaction in movies quantitatively evaluate the MCAEM model and compare it with prior work. Also, a user study is conducted to investigate the benefits of integrating the MCAEM model into social companions. The results demonstrate that the companions equipped with the MCAEM model not only have better retrieval performance, but also improve user experience in many aspects.

Journal

The Visual ComputerSpringer Journals

Published: May 10, 2018

References

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