Continuous categories for a mobile robot

Michael T. Rosenstein, Paul R. Cohen

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

18 Scopus citations

Abstract

Autonomous agents make frequent use of knowledge in the form of categories - categories of objects, human gestures, web pages, and so on. This paper describes a way for agents to learn such categories for themselves through interaction with the environment. In particular, the learning algorithm transforms raw sensor readings into clusters of time series that have predictive value to the agent. We address several issues related to the use of an uninterpreted sensory apparatus and show specific examples where a Pioneer 1 mobile robot interacts with objects in a cluttered laboratory setting.

Original languageEnglish (US)
Title of host publicationProceedings of the National Conference on Artificial Intelligence
PublisherAAAI
Pages634-640
Number of pages7
ISBN (Print)0262511061
StatePublished - Jan 1 1999
EventProceedings of the 1999 16th National Conference on Artificial Intelligence (AAAI-99), 11th Innovative Applications of Artificial Intelligence Conference (IAAI-99) - Orlando, FL, USA
Duration: Jul 18 1999Jul 22 1999

Publication series

NameProceedings of the National Conference on Artificial Intelligence

Other

OtherProceedings of the 1999 16th National Conference on Artificial Intelligence (AAAI-99), 11th Innovative Applications of Artificial Intelligence Conference (IAAI-99)
CityOrlando, FL, USA
Period7/18/997/22/99

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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  • Cite this

    Rosenstein, M. T., & Cohen, P. R. (1999). Continuous categories for a mobile robot. In Proceedings of the National Conference on Artificial Intelligence (pp. 634-640). (Proceedings of the National Conference on Artificial Intelligence). AAAI.