Contentful mental states for robot baby

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

Research output: Contribution to conferencePaper

16 Scopus citations

Abstract

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)
Pages126-131
Number of pages6
StatePublished - Dec 1 2002
Event18th National Conference on Artificial Intelligence (AAAI-02), 14th Innovative Applications of Artificial Intelligence Conference (IAAI-02) - Edmonton, Alta., Canada
Duration: Jul 28 2002Aug 1 2002

Other

Other18th National Conference on Artificial Intelligence (AAAI-02), 14th Innovative Applications of Artificial Intelligence Conference (IAAI-02)
CountryCanada
CityEdmonton, Alta.
Period7/28/028/1/02

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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  • Cite this

    Cohen, P. R., Oates, T., Beal, C. R., & Adams, N. (2002). Contentful mental states for robot baby. 126-131. Paper presented at 18th National Conference on Artificial Intelligence (AAAI-02), 14th Innovative Applications of Artificial Intelligence Conference (IAAI-02), Edmonton, Alta., Canada.