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Encyclopedia of Complexity and Systems Science pp 344–368Cite as

Artificial Intelligence in Modeling and Simulation

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Definition of the Subject

This article discusses the role of Artificial Intelligence (AI) in Modeling and Simulation (M&S). AI is the field of computer science thatattempts to construct computer systems that emulate human problem solving behavior with the goal of understanding human intelligence. M&S isa multidisciplinary field of systems engineering, software engineering, and computer science that seeks to develop robust methodologies forconstructing computerized models with the goal of providing tools that can assist humans in all activities of the M&S enterprise. Although each ofthese disciplines has its core community there have been numerous intersections and cross‐fertilizations between the two fields. From theperspective of this article, we view M&S as presenting some fundamental and very difficult problems whose solution may benefit from the concepts andtechniques of AI.

Introduction

To state the M&S problems that may benefit from AI we first briefly review a system‐theory...

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Abbreviations

Behavior:

The observable manifestation of an interaction with a system.

DEVS:

Discrete Event System Specification formalism describes models developed for simulation; applications include simulation based testing of collaborative services.

Endomorphic agents:

Agents that contain models of themselves and/or of other endomorphic Agents.

Levels of interoperability:

Levels at which systems can interoperate such as syntactic, semantic and pragmatic. The higher the level, the more effective is information exchange among participants.

Levels of system specification:

Levels at which dynamic input/output systems can be described, known, or specified ranging from behavioral to structural.

Metadata:

Data that describes other data; a hierarchical concept in which metadata are a descriptive abstraction above the data it describes.

Model‐based automation:

Automation of system development and deployment that employs models or system specifications, such as DEVS, to derive artifacts.

Modeling and simulation ontology:

The SES is interpreted as an ontology for the domain of hierarchical, modular simulation models specified with the DEVS formalism.

Net‐centric environment:

Network Centered, typically Internet‐centered or web‐centered information exchange medium.

Ontology:

Language that describes a state of the world from a particular conceptual view and usually pertains to a particular application domain.

Pragmatic frame:

A means of characterizing the consumer's use of the information sent by a producer; formalized using the concept of processing network model.

Pragmatics:

Pragmatics is based on Speech Act Theory and focuses on elucidating the intent of the semantics constrained by a given context. Metadata tags to support pragmatics include Authority, Urgency/Consequences, Relationship, Tense and Completeness.

Predicate logic:

An expressive form of declarative language that can describe ontologies using symbols for individuals, operations, variables, functions with governing axioms and constraints.

Schema:

An advanced form of XML document definition, extends the DTD concept.

Semantics:

Semantics determines the content of messages in which information is packaged. The meaning of a message is the eventual outcome of the processing that it supports.

Sensor:

Device that can sense or detect some aspect of the world or some change in such an aspect.

System specification:

Formalism for describing or specifying a system. There are levels of system specification ranging from behavior to structure.

Service-oriented architecture:

Web service architecture in which services are designed to be (1) accessed without knowledge of their internals through well‐defined interfaces and (2) readily discoverable and composable.

Structure:

The internal mechanism that produces the behavior of a system.

System entity structure:

Ontological basis for modeling and simulation. Its pruned entity structures can describe both static data sets and dynamic simulation models.

Syntax:

Prescribes the form of messages in which information is packaged.

UML:

Unified Modeling Language is a software development language and environment that can be used for ontology development and has tools that map UML specifications into XML.

XML:

eXtensible Markup Language provides a syntax for document structures containing tagged information where tag definitions set up the basis for semantic interpretation.

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Author information

Authors and Affiliations

  1. Arizona Center for Integrative Modeling and Simulation, University of Arizona, Tucson, USA

    Bernard Zeigler

  2. CNRS, Università di Corsica, Corte, France

    Alexandre Muzy

  3. Auburn University, Alabama, USA

    Levent Yilmaz

Authors
  1. Bernard Zeigler
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  2. Alexandre Muzy
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  3. Levent Yilmaz
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Editor information

Editors and Affiliations

  1. RAMTECH LIMITED, 122 Escalle Lane, Larkspur, CA, 94939, USA

    Robert A. Meyers Ph. D. (Editor-in-Chief) (Editor-in-Chief)

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Zeigler, B., Muzy, A., Yilmaz, L. (2009). Artificial Intelligence in Modeling and Simulation. In: Meyers, R. (eds) Encyclopedia of Complexity and Systems Science. Springer, New York, NY. https://doi.org/10.1007/978-0-387-30440-3_24

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