Forecasting complex group behavior via multiple plan recognition

Abstract

Group behavior forecasting is an emergent research and application field in social computing. Most of the existing group behavior forecasting methods have heavily relied on structured data which is usually hard to obtain. To ease the heavy reliance on structured data, in this paper, we propose a computational approach based on the recognition of multiple plans/intentions underlying group behavior.We further conduct human experiment to empirically evaluate the effectiveness of our proposed approach.

Keywords

group behavior forecasting / multiple plan recognition / graph search

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Higher Education Press and Springer-Verlag Berlin Heidelberg