Social context cognition crowd-sourcing and semi-automatic parametrization
Publication Type
Journal Article
Publication Date
5-2016
Abstract
This paper presents a semi‐automatic method of parameterizing an existing social context cognition model. It discusses benefits of the social context cognition models for example in personality modeling and their key issue that is parametrization. It briefly introduces social context cognition model and describes a new method of its crowd‐sourcing‐based parametrization. Later, validation is provided, and ability to recreate social context cognition in the provided samples is presented with good generalization for the unknown cases. Finally, model's stability for the continuous stream of dynamic social context input data is shown. Presented system contributes to the believable agent modeling and social simulations by making much needed applications of social context cognition models easier by addressing the so far unsolved troublesome parametrization issues.
Keywords
automatic parametrization, cognitive modeling, crowd-sourcing, social cognition, social context
Discipline
Computer and Systems Architecture | Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
Computer Animation and Virtual Worlds
Volume
27
Issue
3-4
First Page
330
Last Page
339
ISSN
1546-4261
Identifier
10.1002/cav.1718
Publisher
Wiley: 12 months
Citation
KOCHANOWICZ, Jaroslaw; TAN, Ah-hwee; and THALMANN, Daniel.
Social context cognition crowd-sourcing and semi-automatic parametrization. (2016). Computer Animation and Virtual Worlds. 27, (3-4), 330-339.
Available at: https://ink.library.smu.edu.sg/sis_research/5253
Additional URL
https://doi.org/10.1002/cav.1718