Robot baby 2001

Paul R. Cohen, Tim Oates, Niall Adams, Carole R. Beal

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

5 Scopus citations


In this paper we claim that meaningful representations can be learned by programs, although today they are almost always designed by skilled engineers. We discuss several kinds of meaning that representations might have, and focus on a functional notion of meaning as appropriate for programs to learn. Specifically, a representation is meaningful if it incorporates an indicator of external conditions and if the indicator relation informs action. We survey methods for inducing kinds of representations we call structural abstractions. Prototypes of sensory time series are one kind of structural abstraction, and though they are not denoting or compositional, they do support planning. Deictic representations of objects and prototype representations of words enable a program to learn the denotational meanings of words. Finally, we discuss two algorithms designed to find the macroscopic structure of episodes in a domain-independent way.

Original languageEnglish (US)
Title of host publicationAlgorithmic Learning Theory - 12th International Conference, ALT 2001, Proceedings
EditorsNaoki Abe, Roni Khardon, Thomas Zeugmann
Number of pages25
ISBN (Print)3540428755, 9783540428756
StatePublished - 2001
Externally publishedYes
Event12th Annual Conference on Algorithmic Learning Theory, ALT 2001 - Washington, United States
Duration: Nov 25 2001Nov 28 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


Other12th Annual Conference on Algorithmic Learning Theory, ALT 2001
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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