Learning Relational Grammars from Sequences of Actions

Authors:
Blanca Vargas-Govea;Eduardo F. Morales
Affiliations:
Computer Science Department, National Institute of Astrophysics Optics and Electronics, Tonantzintla, México 72840;Computer Science Department, National Institute of Astrophysics Optics and Electronics, Tonantzintla, México 72840
Venue:
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Year:
2009

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Abstract

Many tasks can be described by sequences of actions that normally exhibit some form of structure and that can be represented by a grammar. This paper introduces FOSeq, an algorithm that learns grammars from sequences of actions. The sequences are given as low-level traces of readings from sensors that are transformed into a relational representation. Given a transformed sequence, FOSeq identifies frequent sub-sequences of n -items, or n -grams, to generate new grammar rules until no more frequent n -grams can be found. From m sequences of the same task, FOSeq generates m grammars and performs a generalization process over the best grammar to cover most of the sequences. The grammars induced by FOSeq can be used to perform a particular task and to classify new sequences. FOSeq was tested on robot navigation tasks and on gesture recognition with competitive performance against other approaches based on Hidden Markov Models.