Artificial general segmentation

Daniel Hewlett, Paul R Cohen

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

1 Scopus citations

Abstract

We argue that the ability to find meaningful chunks in sequential input is a core cognitive ability for artificial general intelligence, and that the Voting Experts algorithm, which searches for an information theoretic signature of chunks, provides a general implementation of this ability. In support of this claim, we demonstrate that VE successfully finds chunks in a wide variety of domains, solving such diverse tasks as word segmentation and morphology in multiple languages, visually recognizing letters in text, finding episodes in sequences of robot actions, and finding boundaries in the instruction of an AI student. We also discuss further desirable attributes of a general chunking algorithm, and show that VE possesses them.

Original languageEnglish (US)
Title of host publicationArtificial General Intelligence - Proceedings of the Third Conference on Artificial General Intelligence, AGI 2010
Pages31-36
Number of pages6
Publication statusPublished - 2010
Event3rd Conference on Artificial General Intelligence, AGI 2010 - Lugano, Switzerland
Duration: Mar 5 2010Mar 8 2010

Other

Other3rd Conference on Artificial General Intelligence, AGI 2010
CountrySwitzerland
CityLugano
Period3/5/103/8/10

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ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Hewlett, D., & Cohen, P. R. (2010). Artificial general segmentation. In Artificial General Intelligence - Proceedings of the Third Conference on Artificial General Intelligence, AGI 2010 (pp. 31-36)