# Fact Probability Vector Based Goal Recognition

arXiv: https://arxiv.org/abs/2408.14224 · HTML: https://mlsanity.com/paper/2408.14224/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 26 Aug 2024.

## Builds on (most cited first, 4 of 4)

- [Landmark-Based Approaches for Goal Recognition as Planning](https://mlsanity.com/paper/1904.11739/) (2019)
- [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)
- [Accounting for Context in Plan Recognition, with Application to Traffic Monitoring](https://mlsanity.com/paper/1302.4980/) (2013)

## Built on by (most cited first, 1 of 1)

- [Planning as Goal Recognition: Deriving Heuristics from Intention Models -- Extended Version](https://mlsanity.com/paper/2603.14824/) (2026)

## Abstract (arXiv)

We present a new approach to goal recognition that involves comparing observed facts with their expected probabilities. These probabilities depend on a specified goal g and initial state s0. Our method maps these probabilities and observed facts into a real vector space to compute heuristic values for potential goals. These values estimate the likelihood of a given goal being the true objective of the observed agent. As obtaining exact expected probabilities for observed facts in an observation sequence is often practically infeasible, we propose and empirically validate a method for approximating these probabilities. Our empirical results show that the proposed approach offers improved goal recognition precision compared to state-of-the-art techniques while reducing computational complexity.