Goal Recognition as a Deep Learning Task: the GRNet Approach

TL;DR

This paper introduces GRNet, a deep learning approach that formulates goal recognition as a classification task using RNNs, achieving higher accuracy and faster performance than traditional planning-based methods.

Contribution

The paper presents GRNet, a novel deep learning model for goal recognition that requires only action traces as input, bypassing the need for domain models and improving accuracy and speed.

Findings

GRNet outperforms state-of-the-art goal recognition systems on benchmark datasets.

It achieves higher classification accuracy with reduced runtime.

The approach simplifies goal recognition by using only action traces as input.

Abstract

In automated planning, recognising the goal of an agent from a trace of observations is an important task with many applications. The state-of-the-art approaches to goal recognition rely on the application of planning techniques, which requires a model of the domain actions and of the initial domain state (written, e.g., in PDDL). We study an alternative approach where goal recognition is formulated as a classification task addressed by machine learning. Our approach, called GRNet, is primarily aimed at making goal recognition more accurate as well as faster by learning how to solve it in a given domain. Given a planning domain specified by a set of propositions and a set of action names, the goal classification instances in the domain are solved by a Recurrent Neural Network (RNN). A run of the RNN processes a trace of observed actions to compute how likely it is that each domain proposition is part of the agent's goal, for the problem instance under considerations. These predictions are then aggregated to choose one of the candidate goals. The only information required as input of the trained RNN is a trace of action labels, each one indicating just the name of an observed action. An experimental analysis confirms that \our achieves good performance in terms of both goal classification accuracy and runtime, obtaining better performance w.r.t. a state-of-the-art goal recognition system over the considered benchmarks.