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[2602.14252] GRAIL: Goal Recognition Alignment through Imitation Learning
Summary
The paper introduces GRAIL, a method for recognizing agent goals through imitation learning, enhancing goal recognition accuracy in AI systems by addressing limitations of existing methods.
Why It Matters
Understanding agent goals is crucial for aligning AI with human intentions. GRAIL improves goal recognition in uncertain environments, making AI systems more reliable and effective, especially in real-world applications where behavior may be suboptimal or noisy.
Key Takeaways
- GRAIL utilizes imitation learning to enhance goal recognition accuracy.
- It addresses the limitations of traditional goal-oriented policy representations.
- The method shows significant improvements in F1-scores across various behavioral scenarios.
- GRAIL retains one-shot inference capabilities while accommodating suboptimal behaviors.
- This work contributes to developing robust models for interpreting agent goals.
Computer Science > Artificial Intelligence arXiv:2602.14252 (cs) [Submitted on 15 Feb 2026] Title:GRAIL: Goal Recognition Alignment through Imitation Learning Authors:Osher Elhadad, Felipe Meneguzzi, Reuth Mirsky View a PDF of the paper titled GRAIL: Goal Recognition Alignment through Imitation Learning, by Osher Elhadad and 2 other authors View PDF HTML (experimental) Abstract:Understanding an agent's goals from its behavior is fundamental to aligning AI systems with human intentions. Existing goal recognition methods typically rely on an optimal goal-oriented policy representation, which may differ from the actor's true behavior and hinder the accurate recognition of their goal. To address this gap, this paper introduces Goal Recognition Alignment through Imitation Learning (GRAIL), which leverages imitation learning and inverse reinforcement learning to learn one goal-directed policy for each candidate goal directly from (potentially suboptimal) demonstration trajectories. By scoring an observed partial trajectory with each learned goal-directed policy in a single forward pass, GRAIL retains the one-shot inference capability of classical goal recognition while leveraging learned policies that can capture suboptimal and systematically biased behavior. Across the evaluated domains, GRAIL increases the F1-score by more than 0.5 under systematically biased optimal behavior, achieves gains of approximately 0.1-0.3 under suboptimal behavior, and yields improvements of up to 0...
