Recognizing Plans by Learning Embeddings from Observed Action Distributions

Yantian Zha, Yikang Li, Sriram Gopalakrishnan, Baoxin Li, Subbarao Kambhampati

Abstract: Automated video surveillance requires the recognition of agent plans from videos. One promising direction for plan recognition involves learning shallow action affinity models from plan traces. Extracting such traces from raw video involves uncertainty about the actions. One solution is to represent traces as sequences of action distributions. To use such a representation in approximate plan recognition, we need embeddings of these action distributions. To address this problem, we propose a distribution to vector (Distr2Vec) model, which learns embeddings of action distributions using KL-divergence as the loss function.