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Goal Recognition is the task of discerning the correct intended goal that anagent aims to achieve, given a set of goal hypotheses, a domain model, and asequence of observations (i.e., a sample of the plan executed in theenvironment). Existing approaches assume that goal hypotheses comprise a singleconjunctive formula over a single final state and that the environment dynamicsare deterministic, preventing the recognition of temporally extended goals inmore complex settings. In this paper, we expand goal recognition to temporallyextended goals in Fully Observable Non-Deterministic (FOND) planning domainmodels, focusing on goals on finite traces expressed in Linear Temporal Logic(LTLf) and Pure Past Linear Temporal Logic (PLTLf). We develop the firstapproach capable of recognizing goals in such settings and evaluate it usingdifferent LTLf and PLTLf goals over six FOND planning domain models. Empiricalresults show that our approach is accurate in recognizing temporally extendedgoals in different recognition settings.

Temporally Extended Goal Recognition in Fully Observable Non-Deterministic Domain Models