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.

本文研究将目标识别扩展到具有完整可观察性和非确定性的计划领域模型中，重点是在使用线性时间逻辑（LTLf）和纯过去线性时间逻辑（PLTLf）表达的有限痕迹上识别目标。我们开发了第一种能够识别此类设置中目标的方法，并使用六个计划领域模型上的不同 LTLf 和 PLTLf 目标进行评估，实验结果表明我们的方法在不同的识别设置中识别时间上延长的目标是准确的。