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The entropy reduction engine: integrating planning, scheduling, and control

Authors:

Smadar KedarAuthors Info & Claims

ACM SIGART Bulletin, Volume 2, Issue 4

Pages 61 - 65

https://doi.org/10.1145/122344.122355

Published: 01 July 1991 Publication History

Abstract

This paper describes the Entropy Reduction Engine, an architecture for the integration of planning, scheduling, and control. The architecture is motivated, presented, and analyzed in terms of its different components; namely, problem reduction, temporal projection, and situated control rule execution. Experience with this architecture has motivated the recent integration of learning, and this paper also describes the learning methods and their impact on architecture performance.

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Cited By

Várkonyi-Kóczy A(2013)Fuzzy Approaches in Anytime SystemsOn Fuzziness10.1007/978-3-642-35644-5_43(725-735)Online publication date: 2013
https://doi.org/10.1007/978-3-642-35644-5_43
Iñigo-Blasco PDiaz-del-Rio FRomero-Ternero MCagigas-Muñiz DVicente-Diaz S(2012)Robotics software frameworks for multi-agent robotic systems developmentRobotics and Autonomous Systems10.1016/j.robot.2012.02.00460:6(803-821)Online publication date: 1-Jun-2012
https://dl.acm.org/doi/10.1016/j.robot.2012.02.004
Kiss DVarkonyi-Koczy ARovid A(2011)Improvement for anytime systems with modular architecture2011 IEEE 12th International Symposium on Computational Intelligence and Informatics (CINTI)10.1109/CINTI.2011.6108568(555-559)Online publication date: Nov-2011
https://doi.org/10.1109/CINTI.2011.6108568
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The entropy reduction engine: integrating planning, scheduling, and control

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Published In

cover image ACM SIGART Bulletin

ACM SIGART Bulletin Volume 2, Issue 4

Aug. 1991

221 pages

ISSN:0163-5719

DOI:10.1145/122344

Editor:
Stuart Shapiro
SUNY at Buffalo, Buffalo, NY

Issue’s Table of Contents

Copyright © 1991 Authors.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 July 1991

Published in SIGAI Volume 2, Issue 4

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Cited By

Várkonyi-Kóczy A(2013)Fuzzy Approaches in Anytime SystemsOn Fuzziness10.1007/978-3-642-35644-5_43(725-735)Online publication date: 2013
https://doi.org/10.1007/978-3-642-35644-5_43
Iñigo-Blasco PDiaz-del-Rio FRomero-Ternero MCagigas-Muñiz DVicente-Diaz S(2012)Robotics software frameworks for multi-agent robotic systems developmentRobotics and Autonomous Systems10.1016/j.robot.2012.02.00460:6(803-821)Online publication date: 1-Jun-2012
https://dl.acm.org/doi/10.1016/j.robot.2012.02.004
Kiss DVarkonyi-Koczy ARovid A(2011)Improvement for anytime systems with modular architecture2011 IEEE 12th International Symposium on Computational Intelligence and Informatics (CINTI)10.1109/CINTI.2011.6108568(555-559)Online publication date: Nov-2011
https://doi.org/10.1109/CINTI.2011.6108568
Langley PLaird JRogers S(2009)Cognitive architecturesCognitive Systems Research10.1016/j.cogsys.2006.07.00410:2(141-160)Online publication date: 1-Jun-2009
https://dl.acm.org/doi/10.1016/j.cogsys.2006.07.004
Sapena OOnaindía E(2008)Planning in highly dynamic environmentsApplied Intelligence10.1007/s10489-007-0083-x29:1(90-109)Online publication date: 1-Aug-2008
https://dl.acm.org/doi/10.1007/s10489-007-0083-x
Rillo M(1992)Expectation-Based Temporal Projection SystemProceedings of the Third AnnualConference of AI, Simulation, and Planning in High Autonomy Syslems 'Integrating Perception, Planning and Action'.10.1109/AIHAS.1992.636896(276-281)Online publication date: 1992
https://doi.org/10.1109/AIHAS.1992.636896
Kedar SMcKusick K(1992)Tradeoffs in the Utility of Learned KnowledgeArtificial Intelligence Planning Systems10.1016/B978-0-08-049944-4.50043-4(281-282)Online publication date: 1992
https://doi.org/10.1016/B978-0-08-049944-4.50043-4
Kedar SBresina JDent C(1991)The Blind Leading the Blind: Mutual Refinement of Approximate TheoriesMachine Learning Proceedings 199110.1016/B978-1-55860-200-7.50064-7(308-312)Online publication date: 1991
https://doi.org/10.1016/B978-1-55860-200-7.50064-7

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