Fluent learning: Elucidating the structure of episodes

Paul R. Cohen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationAdvances in Intelligent Data Analysis - 4th International Conference, IDA 2001, Proceedings
EditorsFrank Hoffmann, Gabriela Guimaraes, David J. Hand, Niall Adams, Douglas Fisher
Number of pages10
ISBN (Print)3540425810, 3540425810, 9783540425816, 9783540425816
StatePublished - 2001
Externally publishedYes
Event4th International Conference on Intelligent Data Analysis, IDA 2001 - Cascais, Portugal
Duration: Sep 13 2001Sep 15 2001

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other4th International Conference on Intelligent Data Analysis, IDA 2001

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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