Probabilistic Active Goal Recognition
This work addresses the need for more interactive and adaptive multi-agent systems by enabling agents to actively gather information to reduce uncertainty in goal inference, representing an incremental improvement over prior passive approaches.
The paper tackles the problem of inferring other agents' hidden goals in multi-agent environments by proposing an active goal recognition framework that combines joint belief updates with Monte Carlo Tree Search, showing that it significantly outperforms passive methods in grid-based evaluations.
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.