Discovering Underlying Plans Based on Shallow Models
  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 that 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.
Specifically, we propose two approaches, DUP and RNNPlanner, to discover target
plans based on vector representations of actions. DUP explores the EM-style
framework to capture local contexts of actions and discover target plans by
optimizing the probability of target plans, while RNNPlanner aims to leverage
long-short term contexts of actions based on RNNs (recurrent neural networks)
framework to help recognize target plans. In the experiments, we empirically
show that our approaches are capable of discovering underlying plans that are
not from plan libraries, without requiring domain models provided. We
demonstrate the effectiveness of our approaches by comparing its performance to
traditional plan recognition approaches in three planning domains. We also
compare DUP and RNNPlanner to see their advantages and disadvantages.
