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MAX: a meta-reasoning architecture for “X”

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Daniel R. KuokkaAuthors Info & Claims

ACM SIGART Bulletin, Volume 2, Issue 4

Pages 93 - 97

https://doi.org/10.1145/122344.122363

Published: 01 July 1991 Publication History

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Abstract

This article summarizes the MAX system, a meta-reasoning architecture designed to integrate the reasoning, execution, and learning required of intelligent agents. MAX is based on the idea of specifying all the reasoning and learning capabilities of a system as explicit, declarative knowledge. The mechanisms that support this, as well as the benefits and drawbacks are outlined. An example of the integrated behavior implemented in MAX is given, along with comparisons to related architectures.

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Cited By

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Caro MCox MToscano-Miranda R(2022)A Validated Ontology for Metareasoning in Intelligent SystemsJournal of Intelligence10.3390/jintelligence1004011310:4(113)Online publication date: 24-Nov-2022
https://doi.org/10.3390/jintelligence10040113
Rothfuss JFerreira FAksoy EZhou YAsfour T(2018)Deep Episodic Memory: Encoding, Recalling, and Predicting Episodic Experiences for Robot Action ExecutionIEEE Robotics and Automation Letters10.1109/LRA.2018.28600573:4(4007-4014)Online publication date: Oct-2018
https://doi.org/10.1109/LRA.2018.2860057
Kotseruba ITsotsos J(2018)40 years of cognitive architectures: core cognitive abilities and practical applicationsArtificial Intelligence Review10.1007/s10462-018-9646-y53:1(17-94)Online publication date: 28-Jul-2018
https://dl.acm.org/doi/10.1007/s10462-018-9646-y
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Index Terms

MAX: a meta-reasoning architecture for “X”
1. Computer systems organization
  1. Architectures
    1. Other architectures
2. Computing methodologies

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Association for Computing Machinery

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Published: 01 July 1991

Published in SIGAI Volume 2, Issue 4

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Caro MCox MToscano-Miranda R(2022)A Validated Ontology for Metareasoning in Intelligent SystemsJournal of Intelligence10.3390/jintelligence1004011310:4(113)Online publication date: 24-Nov-2022
https://doi.org/10.3390/jintelligence10040113
Rothfuss JFerreira FAksoy EZhou YAsfour T(2018)Deep Episodic Memory: Encoding, Recalling, and Predicting Episodic Experiences for Robot Action ExecutionIEEE Robotics and Automation Letters10.1109/LRA.2018.28600573:4(4007-4014)Online publication date: Oct-2018
https://doi.org/10.1109/LRA.2018.2860057
Kotseruba ITsotsos J(2018)40 years of cognitive architectures: core cognitive abilities and practical applicationsArtificial Intelligence Review10.1007/s10462-018-9646-y53:1(17-94)Online publication date: 28-Jul-2018
https://dl.acm.org/doi/10.1007/s10462-018-9646-y
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
Radhakrishnan JOntañón SRam A(2009)Goal-driven learning in the GILA integrated intelligence architectureProceedings of the 21st International Joint Conference on Artificial Intelligence10.5555/1661445.1661638(1205-1210)Online publication date: 11-Jul-2009
https://dl.acm.org/doi/10.5555/1661445.1661638
Wray RChong RPhillips JRogers SWalsh WLaird J(1995)Organizing information in MosaicComputer Networks and ISDN Systems10.1016/0169-7552(95)00111-528:1-2(167-178)Online publication date: 1-Dec-1995
https://dl.acm.org/doi/10.1016/0169-7552%2895%2900111-5

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Min-max and robust polynomial optimization

Limit theorems for random MAX-2-XORSAT

A Min--Max Theorem for Bisubmodular Polyhedra

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