GRAML: Goal Recognition As Metric Learning

TL;DR

GRAML introduces a deep metric learning approach for goal recognition that enables quick adaptation to new goals with minimal data, improving speed and flexibility over existing methods.

Contribution

It proposes a novel Siamese network-based method treating goal recognition as metric learning, allowing rapid adaptation to new goals with limited observations.

Findings

Outperforms state-of-the-art in speed and flexibility

Maintains high accuracy with minimal goal data

Effective across diverse environments

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 uses a Siamese network to treat GR as a deep metric learning task, employing an RNN that learns a metric over an embedding space, where the embeddings for observation traces leading to different goals are distant, and embeddings of traces leading to the same goals are close. This metric is especially useful when adapting to new goals, even if given just one example observation trace 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.