@inproceedings{5334fb30017c445db2bb4d705b35e31f,
title = "Robot baby 2001",
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.",
author = "Cohen, {Paul R.} and Tim Oates and Niall Adams and Beal, {Carole R.}",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2001.; 12th Annual Conference on Algorithmic Learning Theory, ALT 2001 ; Conference date: 25-11-2001 Through 28-11-2001",
year = "2001",
doi = "10.1007/3-540-45583-3_4",
language = "English (US)",
isbn = "3540428755",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag",
pages = "32--56",
editor = "Naoki Abe and Roni Khardon and Thomas Zeugmann",
booktitle = "Algorithmic Learning Theory - 12th International Conference, ALT 2001, Proceedings",
}