  This work aims to make plan recognition as planning more ready for real-world
scenarios by adapting previous compilations to work with partial-order,
half-seen observations of both fluents and actions. We first redefine what
observations can be and what it means to satisfy each kind. We then provide a
compilation from plan recognition problem to classical planning problem,
similar to original work by Ramirez and Geffner, but accommodating these more
complex observation types. This compilation can be adapted towards other
planning-based plan recognition techniques. Lastly we evaluate this method
against an "ignore complexity" strategy that uses the original method by
Ramirez and Geffner. Our experimental results suggest that, while slower, our
method is equally or more accurate than baseline methods; our technique
sometimes significantly reduces the size of the solution to the plan
recognition problem, i.e, the size of the optimal goal set. We discuss these
findings in the context of plan recognition problem difficulty and present an
avenue for future work.
