  Goal Recognition is the task of discerning the correct intended goal that an
agent aims to achieve, given a set of goal hypotheses, a domain model, and a
sequence of observations (i.e., a sample of the plan executed in the
environment). Existing approaches assume that goal hypotheses comprise a single
conjunctive formula over a single final state and that the environment dynamics
are deterministic, preventing the recognition of temporally extended goals in
more complex settings. In this paper, we expand goal recognition to temporally
extended goals in Fully Observable Non-Deterministic (FOND) planning domain
models, focusing on goals on finite traces expressed in Linear Temporal Logic
(LTLf) and Pure Past Linear Temporal Logic (PLTLf). We develop the first
approach capable of recognizing goals in such settings and evaluate it using
different LTLf and PLTLf goals over six FOND planning domain models. Empirical
results show that our approach is accurate in recognizing temporally extended
goals in different recognition settings.
