Recognizing LTLf/PLTLf Goals in Fully Observable Non-Deterministic Domain Models

TL;DR

This paper introduces a new method for recognizing temporally extended goals expressed in LTLf and PLTLf within fully observable non-deterministic planning models, demonstrating accurate goal recognition across various domains.

Contribution

It presents a novel approach for goal recognition in FOND models with temporal goals, extending beyond deterministic settings and handling complex temporal logic goals.

Findings

Accurately recognizes temporally extended goals in FOND models.

Effective across multiple domain models and levels of observability.

Demonstrates the practicality of temporal goal recognition in non-deterministic environments.

Abstract

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.