  Recognition in planning seeks to find agent intentions, goals or activities
given a set of observations and a knowledge library (e.g. goal states, plans or
domain theories). In this work we introduce the problem of Online Action
Recognition. It consists in recognizing, in an open world, the planning action
that best explains a partially observable state transition from a knowledge
library of first-order STRIPS actions, which is initially empty. We frame this
as an optimization problem, and propose two algorithms to address it: Action
Unification (AU) and Online Action Recognition through Unification (OARU). The
former builds on logic unification and generalizes two input actions using
weighted partial MaxSAT. The latter looks for an action within the library that
explains an observed transition. If there is such action, it generalizes it
making use of AU, building in this way an AU hierarchy. Otherwise, OARU inserts
a Trivial Grounded Action (TGA) in the library that explains just that
transition. We report results on benchmarks from the International Planning
Competition and PDDLGym, where OARU recognizes actions accurately with respect
to expert knowledge, and shows real-time performance.
