This paper presents preliminary results of our work
with a major financial company, where we try to use
methods of plan recognition in order to investigate the
interactions of a costumer with the company’s online
interface. In this paper, we present the first steps of
integrating a plan recognition algorithm in a real-world
application for detecting and analyzing the interactions
of a costumer. It uses a novel approach for plan recognition
from bare-bone UI data, which reasons about
the plan library at the lowest recognition level in order
to define the relevancy of actions in our domain, and
then uses it to perform plan recognition.
We present preliminary results of inference on three different
use-cases modeled by domain experts from the
company, and show that this approach manages to decrease
the overload of information required from an analyst
to evaluate a costumer’s session — whether this
is a malicious or benign session, whether the intended
tasks were completed, and if not — what actions are
expected next.