Recognizing LTLf/PLTLf Goals in Fully Observable Non-Deterministic Domain Models
This addresses goal recognition for agents with complex temporal goals in non-deterministic environments, representing an incremental advancement over deterministic methods.
The paper tackles the problem of recognizing temporally extended goals, expressed in LTLf/PLTLf, in fully observable non-deterministic domain models, and shows that the approach is accurate at various levels of observability.
Goal Recognition is the task of discerning the correct intended goal that an agent aims to achieve, given a set of possible goals, a domain model, and a sequence of observations as a sample of the plan being executed in the environment. Existing approaches assume that the possible goals are formalized as a conjunction in deterministic settings. In this paper, we develop a novel approach that is capable of recognizing 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 empirically evaluate our goal recognition approach using different LTLf and PLTLf goals over six common FOND planning domain models, and show that our approach is accurate to recognize temporally extended goals at several levels of observability.