Sequential Plan Recognition
  Plan recognition algorithms infer agents' plans from their observed actions.
Due to imperfect knowledge about the agent's behavior and the environment, it
is often the case that there are multiple hypotheses about an agent's plans
that are consistent with the observations, though only one of these hypotheses
is correct. This paper addresses the problem of how to disambiguate between
hypotheses, by querying the acting agent about whether a candidate plan in one
of the hypotheses matches its intentions. This process is performed
sequentially and used to update the set of possible hypotheses during the
recognition process. The paper defines the sequential plan recognition process
(SPRP), which seeks to reduce the number of hypotheses using a minimal number
of queries. We propose a number of policies for the SPRP which use maximum
likelihood and information gain to choose which plan to query. We show this
approach works well in practice on two domains from the literature,
significantly reducing the number of hypotheses using fewer queries than a
baseline approach. Our results can inform the design of future plan recognition
systems that interleave the recognition process with intelligent interventions
of their users.
