Recognizing Plans by Learning Embeddings from Observed Action
  Distributions
  Recent advances in visual activity recognition have raised the possibility of
applications such as automated video surveillance. Effective approaches for
such problems however require the ability to recognize the plans of agents from
video information. Although traditional plan recognition algorithms depend on
access to sophisticated planning domain models, one recent promising direction
involves learning approximated (or shallow) domain models directly from the
observed activity sequences DUP. One limitation is that such approaches expect
observed action sequences as inputs. In many cases involving vision/sensing
from raw data, there is considerable uncertainty about the specific action at
any given time point. The most we can expect in such cases is probabilistic
information about the action at that point. The input will then be sequences of
such observed action distributions. In this work, we address the problem of
constructing an effective data-interface that allows a plan recognition module
to directly handle such observation distributions. Such an interface works like
a bridge between the low-level perception module, and the high-level plan
recognition module. We propose two approaches. The first involves resampling
the distribution sequences to single action sequences, from which we could
learn an action affinity model based on learned action (word) embeddings for
plan recognition. The second is to directly learn action distribution
embeddings by our proposed Distr2vec (distribution to vector) model, to
construct an affinity model for plan recognition.
