Unsupervised clustering of robot activities: A Bayesian approach

Marco Ramoni, Paola Sebastiani, Paul Cohen

Research output: Contribution to conferencePaper

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)
Pages134-135
Number of pages2
StatePublished - Dec 3 2000
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

ASJC Scopus subject areas

  • Engineering(all)

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    Ramoni, M., Sebastiani, P., & Cohen, P. (2000). Unsupervised clustering of robot activities: A Bayesian approach. 134-135. Paper presented at 4th International Conference on Autonomous Agents, Barcelona, Spain, .