Discovering Underlying Plans Based on Distributed Representations of
  Actions
  Plan recognition aims to discover target plans (i.e., sequences of actions)
behind observed actions, with history plan libraries or domain models in hand.
Previous approaches either discover plans by maximally "matching" observed
actions to plan libraries, assuming target plans are from plan libraries, or
infer plans by executing domain models to best explain the observed actions,
assuming complete domain models are available. In real world applications,
however, target plans are often not from plan libraries and complete domain
models are often not available, since building complete sets of plans and
complete domain models are often difficult or expensive. In this paper we view
plan libraries as corpora and learn vector representations of actions using the
corpora; we then discover target plans based on the vector representations. Our
approach is capable of discovering underlying plans that are not from plan
libraries, without requiring domain models provided. We empirically demonstrate
the effectiveness of our approach by comparing its performance to traditional
plan recognition approaches in three planning domains.
