LSTM-Based Goal Recognition in Latent Space

Approaches to goal recognition have progressively relaxed the requirements about the amount of domain knowledge and available observations, yielding accurate and efficient algorithms capable of recognizing goals. However, to recognize goals in raw data, recent approaches require either human enginee...

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Bibliographic Details
Published in:arXiv.org 2018-08
Main Authors: Amado, Leonardo, Aires, João Paulo, Ramon Fraga Pereira, Magnaguagno, Maurício C, Granada, Roger, Meneguzzi, Felipe
Format: Article
Language:English
Subjects:
Algorithms
Coding
Domains
Human behavior
Object recognition
Recurrent neural networks
Online Access:Get full text
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Summary:Approaches to goal recognition have progressively relaxed the requirements about the amount of domain knowledge and available observations, yielding accurate and efficient algorithms capable of recognizing goals. However, to recognize goals in raw data, recent approaches require either human engineered domain knowledge, or samples of behavior that account for almost all actions being observed to infer possible goals. This is clearly too strong a requirement for real-world applications of goal recognition, and we develop an approach that leverages advances in recurrent neural networks to perform goal recognition as a classification task, using encoded plan traces for training. We empirically evaluate our approach against the state-of-the-art in goal recognition with image-based domains, and discuss under which conditions our approach is superior to previous ones.
ISSN:2331-8422