Combining LSTMs and Symbolic Approaches for Robust Plan Recognition

Plan recognition is the task of inferring the actual plan an observed agent is performing to achieve a goal, given domain theory and a partial, possibly noisy, sequence of observations. Applications include natural language processing, elder-care, multi-agent systems, collaborative problem-solving, epistemic problems, and more. Real-world plan recognition problems impose limitations on the quality and quantity of the observations, which may be missing or faulty from silent errors in the sensors. While recent approaches to goal and plan recognition have substantially improved performance under partial observability and noisy conditions, dealing with these problems remains a challenge. Recent work on goal and plan recognition use machine learning to assist planning-based approaches in modeling domains. Such techniques yield robust models capable of accurate predictions with missing or noisy data. Thus inspired, we develop a novel approach to solve both goal and plan recognition tasks simultaneously by combining planning and machine learning techniques to mitigate problems of low and faulty observability.