Leveraging Planning Landmarks for Hybrid Online Goal Recognition

TL;DR

This paper introduces a hybrid online goal recognition method combining symbolic planning landmarks and data-driven techniques, demonstrating improved efficiency and accuracy in real-world scenarios like cooking.

Contribution

It presents a novel hybrid approach that integrates planning landmarks with data-driven methods for faster, more accurate online goal recognition in real-world applications.

Findings

Significantly faster computation time than state-of-the-art methods.

Improved goal recognition accuracy in real-world scenarios.

Planning landmarks are effective beyond artificial benchmarks.

Abstract

Goal recognition is an important problem in many application domains (e.g., pervasive computing, intrusion detection, computer games, etc.). In many application scenarios it is important that goal recognition algorithms can recognize goals of an observed agent as fast as possible and with minimal domain knowledge. Hence, in this paper, we propose a hybrid method for online goal recognition that combines a symbolic planning landmark based approach and a data-driven goal recognition approach and evaluate it in a real-world cooking scenario. The empirical results show that the proposed method is not only significantly more efficient in terms of computation time than the state-of-the-art but also improves goal recognition performance. Furthermore, we show that the utilized planning landmark based approach, which was so far only evaluated on artificial benchmark domains, achieves also good recognition performance when applied to a real-world cooking scenario.