Unsupervised clustering of robot activities: A Bayesian approach

Marco Ramoni, Paola Sebastiani, Paul R Cohen

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

2 Scopus citations

Abstract

Our goal is for robots to learn conceptual systems sufficient for natural language and planning. The learning should be autonomous, without supervision. The first steps in building a conceptual system are to say some things are alike and others are different, based on how an agent interacts with them, and to organize similar things into classes or clusters. We use the BCD algorithm for clustering episodes experienced by our robots. The clusters contain episodes with similar dynamics, described by Markov chains.

Original languageEnglish (US)
Title of host publicationProceedings of the International Conference on Autonomous Agents
Pages134-135
Number of pages2
Publication statusPublished - 2000
Externally publishedYes
Event4th International Conference on Autonomous Agents - Barcelona, Spain
Duration: Jun 3 2000Jun 7 2000

Other

Other4th International Conference on Autonomous Agents
CityBarcelona, Spain
Period6/3/006/7/00

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ASJC Scopus subject areas

  • Engineering(all)

Cite this

Ramoni, M., Sebastiani, P., & Cohen, P. R. (2000). Unsupervised clustering of robot activities: A Bayesian approach. In Proceedings of the International Conference on Autonomous Agents (pp. 134-135)