Learning elements of representations for redescribing robot experiences

Laura Firoiu, Paul Cohen

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

1 Scopus citations


This paper presents our first efforts toward learning simple logical representations from robot sensory data and thus toward a solution for the perceptual grounding problem [2]. The elements of representations learned by our method are states that correspond to stages during the robot’s experiences, and atomic propositions that describe the states. The states are found by an incremental hidden Markov model induction algorithm; the atomic propositions are immediate generalizations of the probability distributions that characterize the states. The state induction algorithm is guided by the minimum description length criterion: the time series of the robot’s sensor values for several experiences are redescribed in terms of states and atomic propositions and the model that yields the shortest description (of both model and time series) is selected.

Original languageEnglish (US)
Title of host publicationAdvances in Intelligent Data Analysis - 3rd International Symposium, IDA 1999, Proceedings
EditorsDavid J. Hand, Joost N. Kok, Michael R. Berthold
Number of pages12
ISBN (Print)3540663320, 9783540663324
StatePublished - 1999
Externally publishedYes
Event3rd International Symposium on Intelligent Data Analysis, IDA 1999 - Amsterdam, Netherlands
Duration: Aug 9 1999Aug 11 1999

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


Other3rd International Symposium on Intelligent Data Analysis, IDA 1999

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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