Goal Recognition over Imperfect Domain Models
  Goal recognition is the problem of recognizing the intended goal of
autonomous agents or humans by observing their behavior in an environment. Over
the past years, most existing approaches to goal and plan recognition have been
ignoring the need to deal with imperfections regarding the domain model that
formalizes the environment where autonomous agents behave. In this thesis, we
introduce the problem of goal recognition over imperfect domain models, and
develop solution approaches that explicitly deal with two distinct types of
imperfect domains models: (1) incomplete discrete domain models that have
possible, rather than known, preconditions and effects in action descriptions;
and (2) approximate continuous domain models, where the transition function is
approximated from past observations and not well-defined. We develop novel goal
recognition approaches over imperfect domains models by leveraging and adapting
existing recognition approaches from the literature. Experiments and evaluation
over these two types of imperfect domains models show that our novel goal
recognition approaches are accurate in comparison to baseline approaches from
the literature, at several levels of observability and imperfections.
