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Probabilistic Active Goal Recognition

Created by
  • Haebom

Author

Chenyuan Zhang, Cristian Rojas Cardenas, Hamid Rezatofighi, Mor Vered, Buser Say

Outline

This paper addresses the problem of goal recognition in multi-agent environments. Unlike previous research on passive goal recognition, we focus on active goal recognition (AGR) and propose a strategic information gathering method to reduce uncertainty. Based on a probabilistic framework, we present a solution that integrates a joint belief updating mechanism and a Monte Carlo Tree Search (MCTS) algorithm. This enables efficient planning and inference of hidden goals of agents without domain-specific knowledge. Experimental results in a grid-based domain demonstrate that the proposed joint belief updating mechanism outperforms passive goal recognition, and the domain-independent MCTS algorithm performs similarly to domain-specific greedy baselines. This demonstrates that the proposed solution is a practical and robust goal inference framework, laying the foundation for more interactive and adaptive multi-agent systems.

Takeaways, Limitations

Takeaways:
We present an efficient and robust framework for active goal recognition (AGR).
Enables goal inference without domain-specific knowledge.
Experimentally verifying the superiority of the joint belief update mechanism.
Contributes to improving the interaction and adaptability of multi-agent systems.
Limitations:
Experiments were conducted only in grid-based domains, requiring further research on generalizability.
The MCTS algorithm can be computationally complex. Further optimization is needed for real-time applications.
There is a need to verify the behavioral models of various agents and their applicability to complex environments.
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