2026/05/08 by Douglas Tesch, Nathan Gavenski, Leonardo Amado +2
Computer Science · #AI-based Problem Solving and Planning #Dynamic time warping #Encoding (memory) #Feature (linguistics) #Key (lock) #Machine Learning in Healthcare #Path (computing) #Pattern recognition (psychology) #Representation (politics) #Signature (topology) #State (computer science) #Time Series Analysis and Forecasting #cs.AI
paper · pdf · open access · doi:10.48550/arxiv.2605.07736
published in Research Portal (King's College London) (King's College London) · Accepted as part of the 35th International Joint Conference on Artificial Intelligence
openalex publication_date 2026/05/08 · openalex created_date 2026/05/12 · openalex updated_date 2026/07/29 · arxiv created 2026/08/05 · arxiv updated 2026/08/06
Online goal recognition in continuous domains poses two central challenges: efficiently encoding large trajectories and effectively comparing them. Recent work addresses these challenges by using custom state-space representations and metrics to compare observations against hypotheses. However, these approaches often overlook well-established encoding techniques used in other domains that offer substantial advantages. This paper introduces a novel method for online goal recognition that leverages path signatures, a compact, expressive representation of rough path theory that efficiently captures key semantic features of trajectories, enabling more meaningful comparisons between them. Experiments show that our method consistently outperforms the state of the art in predictive accuracy and online planning efficiency, while remaining competitive offline.