@inproceedings{7492919b304148c0bb319dca07172c7a,
title = "Fluent learning: Elucidating the structure of episodes",
abstract = "Fluents are logical descriptions of situations that persist, and composite fluents are statistically significant temporal relationships between fluents. This paper presents an algorithm for learning composite fluents incrementally from categorical time series data. The algorithm is tested with a large dataset of mobile robot episodes. It is given no knowledge of the episodic structure of the dataset (i.e., it learns without supervision) yet it discovers fluents that correspond well with episodes.",
author = "Cohen, {Paul R.}",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2001.; 4th International Conference on Intelligent Data Analysis, IDA 2001 ; Conference date: 13-09-2001 Through 15-09-2001",
year = "2001",
doi = "10.1007/3-540-44816-0_27",
language = "English (US)",
isbn = "3540425810",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag",
pages = "268--277",
editor = "Frank Hoffmann and Gabriela Guimaraes and Hand, {David J.} and Niall Adams and Douglas Fisher",
booktitle = "Advances in Intelligent Data Analysis - 4th International Conference, IDA 2001, Proceedings",
}