# Goal recognition via model-based and model-free techniques

arXiv: https://arxiv.org/abs/2011.01832 · HTML: https://mlsanity.com/paper/2011.01832/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2020.

## Built on by (most cited first, 5 of 5)

- [Goal Recognition as a Deep Learning Task: the GRNet Approach](https://mlsanity.com/paper/2210.02377/) (2022)
- [ODGR: Online Dynamic Goal Recognition](https://mlsanity.com/paper/2407.16220/) (2024)
- [Investigating the Combination of Planning-Based and Data-Driven Methods for Goal Recognition](https://mlsanity.com/paper/2301.05608/) (2023)
- [Predicting Customer Goals in Financial Institution Services: A Data-Driven LSTM Approach](https://mlsanity.com/paper/2406.19399/) (2024)
- [GRAML: Goal Recognition As Metric Learning](https://mlsanity.com/paper/2505.03941/) (2025)

## Abstract (arXiv)

Goal recognition aims at predicting human intentions from a trace of observations. This ability allows people or organizations to anticipate future actions and intervene in a positive (collaborative) or negative (adversarial) way. Goal recognition has been successfully used in many domains, but it has been seldom been used by financial institutions. We claim the techniques are ripe for its wide use in finance-related tasks. The main two approaches to perform goal recognition are model-based (planning-based) and model-free (learning-based). In this paper, we adapt state-of-the-art learning techniques to goal recognition, and compare model-based and model-free approaches in different domains. We analyze the experimental data to understand the trade-offs of using both types of methods. The experiments show that planning-based approaches are ready for some goal-recognition finance tasks.