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A multimodel methodology for qualitative model engineering

Editor: Richard Nance Authors:

Paul A. Fishwick,

Bernard P. ZeiglerAuthors Info & Claims

ACM Transactions on Modeling and Computer Simulation (TOMACS), Volume 2, Issue 1

Pages 52 - 81

https://doi.org/10.1145/132277.132280

Published: 02 January 1992 Publication History

Abstract

Qualitative models arising in artificial intelligence domain often concern real systems that are difficult to represent with traditional means. However, some promise for dealing with such systems is offered by research in simulation methodology. Such research produces models that combine both continuous and discrete-event formalisms. Nevertheless, the aims and approaches of the AI and the simulation communities remain rather mutually ill understood. Consequently, there is a need to bridge theory and methodology in order to have a uniform language when either analyzing or reasoning about physical systems. This article introduces a methodology and formalism for developing multiple, cooperative models of physical systems of the type studied in qualitative physics. The formalism combines discrete-event and continuous models and offers an approach to building intelligent machines capable of physical modeling and reasoning.

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Index Terms

A multimodel methodology for qualitative model engineering
1. Computing methodologies

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Published In

cover image ACM Transactions on Modeling and Computer Simulation

ACM Transactions on Modeling and Computer Simulation Volume 2, Issue 1

Jan. 1992

103 pages

ISSN:1049-3301

EISSN:1558-1195

DOI:10.1145/132277

Editor:
Richard Nance
Virginia Tech, Blacksburg

Issue’s Table of Contents

Copyright © 1992 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 January 1992

Published in TOMACS Volume 2, Issue 1

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Cited By

Fard MSarjoughian H(2024)A knowledge interchange broker composition modeling framework for simulating water, energy, and water-energy nexus systemsSimulation10.1177/00375497241233783100:7(657-682)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1177/00375497241233783
Zake MMajore G(2022)Application of Multi-perspective Modelling Approach for Building Digital Twin in Smart Agriculture2022 63rd International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS)10.1109/ITMS56974.2022.9937142(1-7)Online publication date: 6-Oct-2022
https://doi.org/10.1109/ITMS56974.2022.9937142
Brunetti G(2020)Grafting of design-space models onto models of different scope or resolutionJournal of Building Performance Simulation10.1080/19401493.2020.1712477(1-20)Online publication date: 29-Jan-2020
https://doi.org/10.1080/19401493.2020.1712477
Zeigler BMuzy AKofman E(2019)Multi-Formalism Modeling and SimulationTheory of Modeling and Simulation10.1016/B978-0-12-813370-5.00017-1(223-251)Online publication date: 2019
https://doi.org/10.1016/B978-0-12-813370-5.00017-1
Bartz-Beielstein TZaefferer MPham Q(2018)Optimization via multimodel simulationStructural and Multidisciplinary Optimization10.1007/s00158-018-1934-258:3(919-933)Online publication date: 1-Sep-2018
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Ören TMittal SDurak U(2018)INDUCED EMERGENCE IN COMPUTATIONAL SOCIAL SYSTEMS ENGINEERING: MULTIMODELS AND DYNAMIC COUPLINGS AS METHODOLOGICAL BASISEmergent Behavior in Complex Systems Engineering10.1002/9781119378952.ch9(171-199)Online publication date: 16-Apr-2018
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Liu BVerbraeck A(2017)Multi-resolution modeling based on quotient space and DEVSSimulation Modelling Practice and Theory10.1016/j.simpat.2016.10.00470(36-51)Online publication date: Jan-2017
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Diallo SLynch CPadilla JGore RHuschka TChick S(2016)The impact of modeling paradigms on the outcome of simulation studiesProceedings of the 2016 Winter Simulation Conference10.5555/3042094.3042281(1451-1462)Online publication date: 11-Dec-2016
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