Fluent learning: Elucidating the structure of episodes

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

15 Scopus citations

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.

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
PublisherSpringer-Verlag
Pages268-277
Number of pages10
ISBN (Print)3540425810, 3540425810, 9783540425816, 9783540425816
DOIs
StatePublished - Jan 1 2001
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)
Volume2189
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other4th International Conference on Intelligent Data Analysis, IDA 2001
CountryPortugal
CityCascais
Period9/13/019/15/01

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Fluent learning: Elucidating the structure of episodes'. Together they form a unique fingerprint.

  • Cite this

    Cohen, P. R. (2001). Fluent learning: Elucidating the structure of episodes. In F. Hoffmann, G. Guimaraes, D. J. Hand, N. Adams, & D. Fisher (Eds.), Advances in Intelligent Data Analysis - 4th International Conference, IDA 2001, Proceedings (pp. 268-277). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2189). Springer-Verlag. https://doi.org/10.1007/3-540-44816-0_27