# Probabilistic Active Goal Recognition

arXiv: https://arxiv.org/abs/2507.21846 · HTML: https://mlsanity.com/paper/2507.21846/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 33 references that are in the map.
- First posted on arXiv 29 Jul 2025.

## Builds on (most cited first, 7 of 7)

- [Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms](https://mlsanity.com/paper/1911.10635/) (2019)
- [Online algorithms for POMDPs with continuous state, action, and observation spaces](https://mlsanity.com/paper/1709.06196/) (2017)
- [MuMA-ToM: Multi-modal Multi-Agent Theory of Mind](https://mlsanity.com/paper/2408.12574/) (2024)
- [Active Goal Recognition](https://mlsanity.com/paper/1909.11173/) (2019)
- [Heuristic Online Goal Recognition in Continuous Domains](https://mlsanity.com/paper/1709.09839/) (2017)
- [Monte Carlo Information-Oriented Planning](https://mlsanity.com/paper/2103.11345/) (2021)
- [Active Sensing for Search and Tracking: A Review](https://mlsanity.com/paper/2112.02381/) (2021)

## Built on by (most cited first, 2 of 2)

- [From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligence](https://mlsanity.com/paper/2511.18538/) (2025)
- [JRDB-AVR: An Active Visual Reasoning Benchmark for Embodied Agents in Real-World Environments](https://mlsanity.com/paper/2609.35032/) (2026)

## Abstract (arXiv)

In multi-agent environments, effective interaction hinges on understanding the beliefs and intentions of other agents. While prior work on goal recognition has largely treated the observer as a passive reasoner, Active Goal Recognition (AGR) focuses on strategically gathering information to reduce uncertainty. We adopt a probabilistic framework for Active Goal Recognition and propose an integrated solution that combines a joint belief update mechanism with a Monte Carlo Tree Search (MCTS) algorithm, allowing the observer to plan efficiently and infer the actor's hidden goal without requiring domain-specific knowledge. Through comprehensive empirical evaluation in a grid-based domain, we show that our joint belief update significantly outperforms passive goal recognition, and that our domain-independent MCTS performs comparably to our strong domain-specific greedy baseline. These results establish our solution as a practical and robust framework for goal inference, advancing the field toward more interactive and adaptive multi-agent systems.