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MCAEM : mixed-correlation analysis-based episodic memory for companion–user interactions
- Publication Year :
- 2018
-
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. NRF (Natl Research Foundation, S’pore) Accepted version
- Subjects :
- Episodic Memory
business.industry
Computer science
020207 software engineering
02 engineering and technology
Similarity measure
Machine learning
computer.software_genre
Computer Graphics and Computer-Aided Design
Companion–human Interaction
Computer graphics
User experience design
Robustness (computer science)
Correlation analysis
0202 electrical engineering, electronic engineering, information engineering
Computer science and engineering [Engineering]
020201 artificial intelligence & image processing
Computer Vision and Pattern Recognition
Artificial intelligence
business
Episodic memory
computer
Software
Natural language
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....89a3c12860975a17485958238394f260