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

Additional URL

https://doi.org/10.1002/cav.1718

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