Probabilistic Active Goal Recognition

TL;DR

This paper introduces a probabilistic framework for Active Goal Recognition that enables agents to strategically gather information and infer others' goals efficiently without domain-specific knowledge, improving interaction in multi-agent systems.

Contribution

It presents an integrated probabilistic approach combining joint belief updates with Monte Carlo Tree Search for active goal inference, advancing prior passive methods.

Findings

Joint belief update outperforms passive recognition

Domain-independent MCTS matches domain-specific baselines

Framework is practical and robust for multi-agent interactions

Abstract

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.