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.

In this report, we will define a new approach to the problem of non deterministic planning for extended temporal goals. In particular, we will give a solution to this problem reducing it to a fully observable non deterministic (FOND) planning problem and taking advantage of the LTLfToDFA tool. First of all, we will introduce the main idea and motivations supporting our approach. Then, we will give some preliminaries explaining the Planning Domain Definition Language (PDDL) language and the FOND planning problem formally. After that, we will illustrate our FOND4LTLfPLTLf (also available online) approach with the encoding of temporal goals into a PDDL domain and problem. Finally, we will present some of the results obtained through the application of the proposed solution.