LatRec: Recognizing Goals in Latent Space (Student Abstract)

Abstract

Recent approaches to goal recognition have progressively relaxed the requirements about the amount of domain knowledge and available observations, yielding accurate and efficient algorithms. These approaches, however, assume that there is a domain expert capable of building complete and correct domain knowledge to successfully recognize an agent's goal. This is too strong for most real-world applications. We overcome these limitations by combining goal recognition techniques from automated planning, and deep autoencoders to carry out unsupervised learning to generate domain theories from data streams and use the resulting domain theories to deal with incomplete and noisy observations. Moving forward, we aim to develop a new data-driven goal recognition technique that infers the domain model using the same set of observations used in recognition itself.

Goal Recognition as Reinforcement Learning

“…A large body of work involves learning for planning domains (Zimmerman and Kambhampati 2003;Arora et al 2018). While some approaches learn action models from data, they do not link these action models to policies for reaching specific goals (Amir and Chang 2008;Amado et al 2019;Asai and Muise 2020;Juba, Le, and Stern 2021).…”