# Goal Recognition as a Deep Learning Task: the GRNet Approach

arXiv: https://arxiv.org/abs/2210.02377 · HTML: https://mlsanity.com/paper/2210.02377/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 48 references that are in the map.
- First posted on arXiv 5 Oct 2022.

## Builds on (most cited first, 10 of 10)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [The LAMA Planner: Guiding Cost-Based Anytime Planning with Landmarks](https://mlsanity.com/paper/1401.3839/) (2014)
- [Landmark-Based Approaches for Goal Recognition as Planning](https://mlsanity.com/paper/1904.11739/) (2019)
- [Goal Recognition as Reinforcement Learning](https://mlsanity.com/paper/2202.06356/) (2022)
- [LSTM-Based Goal Recognition in Latent Space](https://mlsanity.com/paper/1808.05249/) (2018)
- [An LP-Based Approach for Goal Recognition as Planning](https://mlsanity.com/paper/1905.04210/) (2019)
- [Goal recognition via model-based and model-free techniques](https://mlsanity.com/paper/2011.01832/) (2020)
- [Cost-Based Goal Recognition Meets Deep Learning](https://mlsanity.com/paper/1911.10074/) (2019)

## Built on by (most cited first, 9 of 9)

- [ODGR: Online Dynamic Goal Recognition](https://mlsanity.com/paper/2407.16220/) (2024)
- [GRAIL: Goal Recognition Alignment through Imitation Learning](https://mlsanity.com/paper/2602.14252/) (2026)
- [General Dynamic Goal Recognition using Goal-Conditioned and Meta Reinforcement Learning](https://mlsanity.com/paper/2505.09737/) (2025)
- [Goal Recognition via Linear Programming](https://mlsanity.com/paper/2404.07934/) (2024)
- [GRAML: Goal Recognition As Metric Learning](https://mlsanity.com/paper/2505.03941/) (2025)
- [Online Dynamic Goal Recognition in Gym Environments](https://mlsanity.com/paper/2509.23244/) (2025)
- [Removing Planner Bias in Goal Recognition Through Multi-Plan Dataset Generation](https://mlsanity.com/paper/2602.14691/) (2026)
- [Zero-Shot Goal Recognition with Large Language Models](https://mlsanity.com/paper/2605.15333/) (2026)
- [Multi-Agent Goal Recognition with Team- and Goal-Conditioned Reinforcement Learning and Factorized Branch-and-Bound](https://mlsanity.com/paper/2606.25978/) (2026)

## Abstract (arXiv)

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.