Towards Efficient Online Goal Recognition through Deep Learning

Online goal recognition (OGR) is the task of understanding the intention of an agent as it executes a plan, recognizing its goal every time it performs a new action. This task is important in different contexts, such as applications of cyber-security and human-robot collaboration. An effective OGR system should (i) compute the correct goal of the agent as early as possible with respect to the agent’s performed actions, and (ii) perform fast, given that, while the OGR system infers the agent’s goal, the agent keeps executing the plan. In this paper, we propose a deep-learning approach to OGR based on Recurrent Neural Networks. The approach is implemented in a new system that learns to predict the goal of an agent acting in a given planning domain using a training dataset for the domain. We propose a method that exploits planning-related knowledge for designing a training dataset that is effective for the OGR and for improving the system performance. An experimental evaluation of our system on several benchmark domains shows that it performs generally better than the state-of-the-art in terms of accuracy and execution time, considering both the requirements (i) and (ii).

Authors

• Lorenzo Serina Information Engineering, Università degli Studi di Brescia
• Mattia Chiari Università degli Studi di Brescia
• Alfonso Emilio Gerevini Università degli Studi di Brescia
• Luca Putelli
• Ivan Serina Paolo Toninelli

Keywords

• Online Goal Recognition
• Deep Learning
• Automated Planning