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This is a Experimental Study study.

September 20, 2025

GRAML: Goal Recognition As Metric Learning

Key Points

graml improves recognition speed and flexibility, enabling quick adaptation to new goals.
This method allows learning in an embedding space where similar goal traces are close, enhancing recognition accuracy.
Evaluated across diverse environments, graml outperforms current goal recognition methods in speed and performance.
It demonstrates the ability to adapt even with a single example trace for new goals, addressing a major limitation.

Abstract

Goal Recognition (GR) is the problem of recognizing an agent's objectives based on observed actions. Recent data-driven approaches for GR alleviate the need for costly, manually crafted domain models. However, these approaches can only reason about a pre-defined set of goals, and time-consuming training is needed for new emerging goals. To keep this model-learning automated while enabling quick adaptation to new goals, this paper introduces GRAML: Goal Recognition As Metric Learning. GRAML frames GR as a deep metric learning problem, using a Siamese network composed of recurrent units to learn an embedding space where traces leading to the same goal are close, and those leading to different goals are distant. This metric is particularly effective for adapting to new goals, even when only a single example trace is available per goal. Evaluated on a versatile set of environments, GRAML shows speed, flexibility, and runtime improvements over the state-of-the-art GR while maintaining accurate recognition.

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