Identifying qualitatively different outcomes of actions: Gaining autonomy through learning

Tim Oates; Matthew D. Schmill; Paul R. Cohen

Identifying qualitatively different outcomes of actions: Gaining autonomy through learning

Tim Oates, Matthew D. Schmill, Paul R. Cohen

Research output: Contribution to conference › Paper › peer-review

Abstract

An unsupervised method for learning models of environmental dynamics based on clustering multivariate time series is presented. Experiments with a Pioneer-1 mobile robot demonstrate the utility of the method. It is shown that the models acquired by the robot correlate surprisingly well with human models of the environment.

Original language	English (US)
Pages	110-111
Number of pages	2
State	Published - 2000
Externally published	Yes
Event	4th International Conference on Autonomous Agents - Barcelona, Spain Duration: Jun 3 2000 → Jun 7 2000

Other

Other	4th International Conference on Autonomous Agents
City	Barcelona, Spain
Period	6/3/00 → 6/7/00

ASJC Scopus subject areas

General Engineering

Cite this

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title = "Identifying qualitatively different outcomes of actions: Gaining autonomy through learning",

abstract = "An unsupervised method for learning models of environmental dynamics based on clustering multivariate time series is presented. Experiments with a Pioneer-1 mobile robot demonstrate the utility of the method. It is shown that the models acquired by the robot correlate surprisingly well with human models of the environment.",

author = "Tim Oates and Schmill, {Matthew D.} and Cohen, {Paul R.}",

year = "2000",

language = "English (US)",

pages = "110--111",

note = "4th International Conference on Autonomous Agents ; Conference date: 03-06-2000 Through 07-06-2000",

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TY - CONF

T1 - Identifying qualitatively different outcomes of actions

T2 - 4th International Conference on Autonomous Agents

AU - Oates, Tim

AU - Schmill, Matthew D.

AU - Cohen, Paul R.

PY - 2000

Y1 - 2000

N2 - An unsupervised method for learning models of environmental dynamics based on clustering multivariate time series is presented. Experiments with a Pioneer-1 mobile robot demonstrate the utility of the method. It is shown that the models acquired by the robot correlate surprisingly well with human models of the environment.

AB - An unsupervised method for learning models of environmental dynamics based on clustering multivariate time series is presented. Experiments with a Pioneer-1 mobile robot demonstrate the utility of the method. It is shown that the models acquired by the robot correlate surprisingly well with human models of the environment.

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M3 - Paper

AN - SCOPUS:0033688592

SP - 110

EP - 111

Y2 - 3 June 2000 through 7 June 2000

ER -

Identifying qualitatively different outcomes of actions: Gaining autonomy through learning

Abstract

Other

ASJC Scopus subject areas

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