DEVS%20Today:%20%20Recent%20Advances%20in%20Discrete%20Event%20-%20%20%20%20%20%20%20%20Based%20Information%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20Technology

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DEVS Today Recent Advances in Discrete Event
- Based Information
Technology

• Bernard P. Zeigler
• Professor, ECE
• Arizona Center for Integrative Modeling and
  Simulation
• University of Arizona
• Tucson
  www.acims.arizona.edu

Keynote Talk to Majestic
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Outline

• Framework for MS
• Discrete Event Processing
• DEVS Formalism
• Implications for Current Practice
• Application Examples
• MS as a Bridge Discipline

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Framework for MS Entities and Relations
Experimental Frame
Device for executing model
Real World
Simulator
Data Input/output relation pairs
Experimental frame specifies conditions under
which the system is experimented with and
observed
Model
Each entity is formalized as a Mathematical
Dynamic System Each relation is represented by
a homomorphism or other equivalence
Structure for generating behavior claimed to
represent real world
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Discrete Event Time Segments
X
t1
t0
t2
S
e
y0
Y
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DEVS Background

• DEVS Discrete Event System Specification
• Based on formal MS framework
• Derived from mathematical dynamical system
  theory
• Supports hierarchical, modular composition
• Object oriented implementation
• Supports discrete and continuous paradigms
• Exploits efficient parallel and distributed
  simulation techniques

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DEVS Hierarchical Modular Composition

• Atomic lowest level model, contains structural
  dynamics -- model level modularity

Coupled composed of one or more atomic and/or
coupled models
coupling
Hierarchical construction
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DEVS Theoretical Properties

• Closure Under Coupling
• Universality for Discrete Event Systems
• Representation of Continuous Systems
• quantization integrator approximation
• pulse representation of wave equations
• Simulator Correctness, Efficiency

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DEVS Expressability
Coupled Models
Atomic Models
Partial Differential Equations
can be components in a coupled model
Ordinary Differential Equation Models
Processing/ Queuing/ Coordinating
Networks, Collaborations
Physical Space
Spiking Neuron Networks
Spiking Neuron Models
Processing Networks
Petri Net Models
n-Dim Cell Space
Discrete Time/ StateChart Models
Stochastic Models
Cellular Automata
Quantized Integrator Models
Self Organized Criticality Models
Fuzzy Logic Models
Reactive Agent Models
Multi Agent Systems
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Coupled model structure
Cell Space
Ignite
Wind
Water
Potential neighbor cells to ignite by fire from
center cell.
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Atomic model structure
Forest Cell State Transitions
Fireline Intensity FI





input

input

input


Make a transition

Make a transition

Make a transition

elapsed

elapsed

time

time

Time advance

Time advance

Phase unburned

If (FI gt Threshold)

Phase burning
holdIn (burning,

else


Compute new spread ( using

passiva
teIn( Unburned)

Rothermels eq)

Compute remaining distance to

reach center of neighbor cell

Compute time delays

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Experimentation
experimental frame
Cell Space
Ignite
Wind
Water
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wind across valley floor experiments

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water meets fire experiment



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MS Framework Implications for Current Practice

• Separate Models From Simulators
• Separate Models From Experimental Frames
• Use the DEVS Formalism for Developing Models,
  Experimental Frames, and Simulators
• Experimental Frames Support Defense Certification
  Testing
• Maintain Repositories of Reusable Models and
  Frames

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Separate Models From Simulators

• Models are goal oriented abstractions of reality.
  
• Simulators are the computational engines that
  drive the models to obtain results.

Currently Simulation software tends to
encapsulate models and simulators in tightly
coupled packages.

• In the MS-Framework-based approach..
• Models and Simulators are treated as distinct
  entities with their own software representations.
• There are simulators for different kinds of
  models that can be selected according to the
  needs of the simulation,
• For example, a simulator might be chosen for its
  efficiency on a single host, or for its ability
  to execute the model on multiple hosts
  (distributed simulation)

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Separate Models From Experimental Frames

• Experimental Frames are specifications of the
  experimentation to be done on a model
• Frames represent the objectives of the
  experimenter, tester, or analyst

Currently Simulation software tends to
encapsulate models, simulators and experimental
frames into tightly coupled packages.

• In the MS-Framework-based approach..
• Models and Experimental Frames are treated as
  distinct entities with their own software
  representations.
• Since the experimental frames appropriate to a
  model are distinctly identified, it is easier for
  potential users of a model to uncover the
  objectives and assumptions that went into its
  creation.

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Use the DEVS Formalism for Developing Models,
Experimental Frames, and Simulators

• The DEVS formalism enables users to develop
  models separately from experimental frames .
• Models and frames can then be coupled together
  and given to an appropriate simulator to execute.

Currently Programming languages such as Fortran,
C, C or Java are used to develop software
packages of strongly coupled models, frames and
simulators.

• In the MS-Framework-based approach..
• The DEVS formalism Is employed for all simulation
  software development.
• DEVS simulators are employed to perform single
  host, distributed and heterogeneous real-time
  execution as needed.
• DEVS simulators exist that run over various
  middleware such as MPI,HLA, CORBA,P2P, and MOM.

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Maintain Repositories of Reusable Models and
Frames

• Models and Experimental Frames can be stored in
  organized repositories to support reuse under
  well specified conditions

Currently There are relatively few examples of
storing previously developed simulation
infrastructure commodities in such a way that
they can be easily adapted to developing
interoperability test requirements

• In the MS-Framework-based approach..
• Repositories of models and frames are created and
  maintained.
• Such repositories foster reuse of existing models
  and frames to serve as components for
  constructing new ones.
• When new models or frames are developed they are
  deposited in the repositories with appropriate
  information to enable their reuse with high
  confidence of success.

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Managed Modeling in Lockheeds System of
Systems MS Environment

• DEVS (Discrete Event Modeling Formalism)
• Separates Model and Simulators
• Defines Couple Models and Atomic Models
• Modularized via Ports and Defined Events
• SES (System Entity Structure)
• Provides a well defined structure for model reuse
• Maintains kind-of, part-of, multiplicity
  relationships
• Supports constraints on model compatibility
• Architecture based on SES/DEVS supports component
  model reuse evolved during last decade

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Component Reusability in Lockheeds DEVS MS
Environment
Project Model Critical Mobile Target Global Positioning System III Arsenal Ship Coast Guard Deep Water Space Operations Vehicle Common Aero Vehicle Joint Composite Tracking Network Integrated System Center Space Based Laser Space Based Discrimination Missile Defense (Theater / National)
RAD x x x x x x x
IR x x x x x x x
MIS x x x x x
LAS x x x x
Comm x x x x x x
CC x x x
Earth x x x x x
WC x x
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DEVS framework for knowledge based control of
steel production

• Sachem large-scale real-time monitor/diagnose
  control system for blast furnace operation
• Usinor -- worlds largest producer of steel
  products, Sachem saves it millions of euros
  annually
• Problems for conventional control and AI
• Experts perception knowledge is implicit,
  concerns dynamic physical processes
• Difficult to model the reasoning of a control
  process expert.
• Lack of mathematical models for blast furnace
  dynamics
• Solution
• time-based perception and discrete event
  processing for dealing with complex dynamical
  systems

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DEVS framework for knowledge based control of
steel production (contd)
quanti zation
signal events
signal pheno mena
process pheno mena

• Large Scale
• Conceptual model contains 25,000 objects for 33
  goals, 27 tasks,etc.
• Approximately 400,000 lines of code.
• 14 man-years 6 knowledge engineers and 12
  experts
• One advantage of DEVS is compactness 50,000
  reduction in data volume

Effective analysis and control of the behavior
of blast furnaces at high resolution
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University of New Mexico Virtual Lab for
Autonomous Agents
V-Lab developed on top of DEVSJAVA includes a
simulation environment for robotic agents with
physics, terrain and dynamics. It extends DEVS
to provide a layer for specifying intelligent
automation and soft computing algorithms (IDEVS).
IDEVS
SimEnv
DEVS Simulator
Middleware (HLA,CORBA,JMS)
Computer Network
V-Lab-a virtual laboratory for autonomous
agents-SLA-based learning controllers El-Osery,
A.I. Burge, J. Jamshidi, M. Saba, A. Fathi,
M. Akbarzadeh-T, M.-R. Systems, Man and
Cybernetics, Part B, IEEE Transactions on ,
Volume 32 Issue 6 , Dec. 2002 Page(s) 791
-803
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Mapping Differential Equation Models into DEVS
Integrator Models
DEVS Integrator
DEVS instantaneous function
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Activity a characteristic of continuous models
Activity f(t1) f(t0)
Number of crossings Activity/quantum
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DEVS Efficiency Advantage where Activity is
Heterogeneous in Time and Space
diffusion
activity
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Activity as unifying continuous and discrete
paradigms
DEVS represents all decision making and
continuous dynamic components in the scene
Heterogeneous activity in time and space
Quantization allows DEVS to naturally focus
computing resources on high activity regions
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Modeling and Simulation as a Bridging Discipline
(3)

• Discrete Systems
• Digital
• Computer Science
• Algorithms

• Continuous Systems
• Analog
• Control theory
• Linear/Non Linear
• ODE/PDEs

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Modeling and Simulation as a Bridging Discipline
(4)

• PADS
• Logical Process
• Time Warp
• Large Numbers
• Network, Agent Apps

• Computational Science
• Numerical Methods
• Supercomputing
• MPI
• PDEs

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More Information

• Zeigler, B.P., Praehofer, H., and Kim, T.G.,
  Theory of Modeling and Simulation, 2nd Edition.
  Academic Press, 2000.
• ACIMS www.acims.arizona.edu DEVSJAVA
  downloadable software
• Society for Modeling and Simulation, Intl.
  www.scs.org
• Simulation Journal,
• new Journal of Defense Modeling and Simulation