Plan recognition, the problem of inferring the goals or plans of an observed agent, is a key element of situation awareness in human-machine and machine-machine interactions for many applications. Some plan recognition algorithms require knowledge about the potential behaviours of the observed agent in the form of a plan library, together with a decision model about how the observed agent uses the plan library to make decisions. It is however difficult to elicit and specify the decision model a priori. In this paper, we present a recursive neural network model that learns such a decision model automatically. We discuss promising experimental results of the approach with comparisons to selected state-of-the-art plan recognition algorithms on three benchmark domains.

The story starts out like a bad joke: Obama, Clinton and Trump walk into a bar, where they applauded a new startup based in Montreal, Canada called Lyrebird. If the scenario seems too bizarre to be real, you're right--it's not. The entire recording was generated by a new AI with the ability to mimic natural conversation, at a rate much faster than any previous speech synthesizer. From there, it adds an extra layer of emotion or special intonation, until it nails a person's voice, tone and accent--may it be Obama, Trump or even you. While Lyrebird still retains a slight but noticeable robotic buzz characteristic of machine-generated speech, add some smartly-placed background noise to cover up the distortion, and the recordings could pass off as genuine to unsuspecting ears.