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Social context cognition crowd‐sourcing and semi‐automatic parametrization
Author(s) -
Kochanowicz Jaroslaw,
Tan AhHwee,
Thalmann Daniel
Publication year - 2016
Publication title -
computer animation and virtual worlds
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.225
H-Index - 49
eISSN - 1546-427X
pISSN - 1546-4261
DOI - 10.1002/cav.1718
Subject(s) - computer science , context (archaeology) , parametrization (atmospheric modeling) , cognition , artificial intelligence , human–computer interaction , cognitive science , psychology , paleontology , physics , quantum mechanics , neuroscience , biology , radiative transfer
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. Copyright © 2016 John Wiley & Sons, Ltd.