Temporally Extended Goal Recognition in Fully Observable Non-Deterministic Domain Models
This addresses a limitation in goal recognition for AI planning by extending it to more complex, non-deterministic environments with temporal logic goals, though it is incremental as it builds on prior work.
The paper tackled the problem of recognizing temporally extended goals in fully observable non-deterministic domains, which existing methods could not handle, and developed the first approach for this, showing accurate results in evaluations across six domain models.
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.