# Active Goal Recognition

arXiv: https://arxiv.org/abs/1909.11173 · HTML: https://mlsanity.com/paper/1909.11173/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2019.

## Built on by (most cited first, 16 of 16)

- [A Survey of Ad Hoc Teamwork Research](https://mlsanity.com/paper/2202.10450/) (2022)
- [World Models and Predictive Coding for Cognitive and Developmental Robotics: Frontiers and Challenges](https://mlsanity.com/paper/2301.05832/) (2023)
- [Expected Value of Communication for Planning in Ad Hoc Teamwork](https://mlsanity.com/paper/2103.01171/) (2021)
- [Towards Collaborative Plan Acquisition through Theory of Mind Modeling in Situated Dialogue](https://mlsanity.com/paper/2305.11271/) (2023)
- [Deceptive Decision-Making Under Uncertainty](https://mlsanity.com/paper/2109.06740/) (2021)
- [Explainable Goal Recognition: A Framework Based on Weight of Evidence](https://mlsanity.com/paper/2303.05622/) (2023)
- [ADESSE: Advice Explanations in Complex Repeated Decision-Making Environments](https://mlsanity.com/paper/2405.20705/) (2024)
- [GRAIL: Goal Recognition Alignment through Imitation Learning](https://mlsanity.com/paper/2602.14252/) (2026)
- [Probabilistic Active Goal Recognition](https://mlsanity.com/paper/2507.21846/) (2025)
- [Inferring Agents Preferences as Priors for Probabilistic Goal Recognition](https://mlsanity.com/paper/2102.11791/) (2021)
- [Generalising Planning Environment Redesign](https://mlsanity.com/paper/2402.07799/) (2024)
- [Towards Intention Recognition for Robotic Assistants Through Online POMDP Planning](https://mlsanity.com/paper/2411.17326/) (2024)
- [Robots Influencing Humans to Reveal their Goals during Collaboration and Competition](https://mlsanity.com/paper/2609.05519/) (2026)
- [Predicting Customer Goals in Financial Institution Services: A Data-Driven LSTM Approach](https://mlsanity.com/paper/2406.19399/) (2024)
- [Uncertainty-Resilient Active Intention Recognition for Robotic Assistants](https://mlsanity.com/paper/2508.19150/) (2025)
- [Autonomous Assessment of Generalizability of AI Agent Capabilities](https://mlsanity.com/paper/2512.16733/) (2025)

## Abstract (arXiv)

To coordinate with other systems, agents must be able to determine what the systems are currently doing and predict what they will be doing in the future---plan and goal recognition. There are many methods for plan and goal recognition, but they assume a passive observer that continually monitors the target system. Real-world domains, where information gathering has a cost (e.g., moving a camera or a robot, or time taken away from another task), will often require a more active observer. We propose to combine goal recognition with other observer tasks in order to obtain active goal recognition (AGR). We discuss this problem and provide a model and preliminary experimental results for one form of this composite problem. As expected, the results show that optimal behavior in AGR problems balance information gathering with other actions (e.g., task completion) such as to achieve all tasks jointly and efficiently. We hope that our formulation opens the door for extensive further research on this interesting and realistic problem.