@inproceedings{Amado2020,
author = {Leonardo Amado and Jo\~{a}o Paulo Aires and Ramon F. Pereira and Maur\'{i}cio C. Magnaguagno and Roger Granada and Gabriel Paludo Licks and Matheus Marcon and Felipe Meneguzzi},
title = {{LatRec+: Learning-based Goal Recognition in Latent Space (Demo)}},
booktitle = {The AAAI 2020 Workshop on Plan, Activity, and Intent Recognition (PAIR@AAAI): Demo Track},
year = {2020},
abstract = {
Existing approaches to goal recognition are able to infer do- main knowledge by combining goal recognition techniques from automated planning, and deep autoencoders to learn do- main theories from data streams. However, most recent approaches to goal recognition in these learned domains struggle with high spread during recognition process. LATREC+ leverages from the usage of learning approaches to recognize goals directly in real-world data (images), without relying on domain theories. The learned model is given a set of observations and returns the probability of each predicate being true. We demonstrate this approach in an online simulation of simple games, such as the n-puzzle game.
}
}