DEVS Today:

Recent Advances inDiscrete Event -

Based Information Technology

Bernard P. ZeiglerBernard P. Zeigler

Professor, ECEProfessor, ECEArizona Center for Integrative Modeling and SimulationArizona Center for Integrative Modeling and SimulationUniversity of ArizonaUniversity of ArizonaTucson Tucson www.acims.arizona.eduwww.acims.arizona.edu

Keynote Talk to MaMajestic

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Outline

• Framework for M&S

• Discrete Event Processing

• DEVS Formalism

• Implications for Current Practice

• Application Examples

• M&S as a Bridge Discipline

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Framework for M&S: Entities and Relations

Real WorldReal World SimulatorSimulator

modelingrelation

simulationrelation

Each entity is formalized as a Mathematical Dynamic System

Each relation is represented by a homomorphism or other equivalence

Structure for generating behaviorclaimed to represent real world

Device forexecuting model

Model

Experimental frame specifies conditions under which the system is experimented with and observed

Experimental Frame

Data: Input/output relation pairs

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x0 x1X

S

Yy0

e

t0 t1 t2

Discrete Event Time Segments

• DEVS = Discrete Event System Specification

• Based on formal M&S 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

DEVS Background

DEVS Hierarchical Modular Composition

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

Atomic

Atomic Atomic

Atomic

+ coupling

Coupled: composed of one or more atomic and/or coupled models

Atomic

Atomic

Atomic

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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Atomic Models

OrdinaryDifferentialEquationModels

Spiking NeuronModels

Coupled Models

Petri NetModels

Cellular Automata

n-Dim Cell Space

PartialDifferentialEquations

Self Organized Criticality

Models

Processing/Queuing/

Coordinating

ProcessingNetworks

Networks,Collaborations Physical

Space

DEVS Expressability

can becomponents in a coupled model

MultiAgent

Systems

Discrete Time/

StateChartModels

QuantizedIntegrator

Models

Spiking Neuron

Networks

Stochastic

Models

ReactiveAgent

Models

Fuzzy Logic

Models

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Cell Space

Wind

Water

Ignite

Coupled model structure

N

E

NENW

W

SW

S SE

Potential neighbor cells to ignite by fire from center cell.

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unburned unburned_wet

burning

burned_wetburning_wet

burned

Fire suppressant

Burning delay = 0

Ignition and

(fireline intensity > Threshold)

Fire suppressant

delay = 0

Fire suppressant and fire fighting rule

satisfied

Forest Cell State Transitions

Atomic model structure

Time advance

input

Make a transition

elapsed time

Time advance

input input

Make a transition Make a transition

elapsed time

Phase “unburned” If (FI > Threshold)

holdIn (“burning”,

else passivateIn( “Unburned)”

Compute new spread ( using

Rothermel’s eq)

Compute remaining distance to reach center of neighbor cell Compute time delays

Fireline IntensityFI

Phase “burning”

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Experimentation

WindFlowModel

Fire Fighting

Model

Forest CellIgniter

Cell Space Display

Transducer

DisplayAverage Rate of Spread &

Direction

DisplayActive Cells

Vs.Total Cells

DisplayOther Stats

Cell Space

Wind

Water

Ignite

experimental frame

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10°15°Wind

Wind Wind N

NFFL-Fuel-Model

5: Brush (2 ft)

NFFL-Fuel-Model 11:

Light logging slash

NFFL-Fuel-Model 7:

Southern rough

wind across valley floor experiments

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water meets fire experiment

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M&S 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.

In the M&S-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)

Currently…Simulation software tends to encapsulate models and simulators in

tightly coupled packages.

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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

In the M&S-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.

Currently…Simulation software tends to encapsulate models, simulators and

experimental frames into tightly coupled packages.

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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.

In the M&S-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.

Currently…Programming languages such as Fortran, C, C++ or Java are used to

develop software packages of strongly coupled models, frames and simulators.

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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

In the M&S-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.

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

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Managed Modeling in Lockheed’s “System of Systems” M&S 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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Project

Model

Critical Mobile Target

Global Positioning System

III

Arsenal Ship

Coast Guard Deep Water

Space Operatio

ns Vehicle

Common Aero Vehicle

Joint Compos

ite 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 xMISMIS x x x x xLAS x x x x

Comm x x x x x xCC x x x

Earth x x x x xWC x x

Component Reusability in Lockheed’s DEVS M&S Environment

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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 -- world’s 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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quantization

signalevents

signalphenomena

processphenomena

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

DEVS framework for knowledge based control of steel production (cont’d)

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University of New Mexico Virtual Lab for Autonomous Agents

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

Physics Terrain Dynamic

SimEnv

Control Agents SimMan

Computer Network

Middleware (HLA,CORBA,JMS)

DEVS Simulator

IDEVS SimEnv

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).

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nD

(n-1)D

X>0

X<0

D

ta(nD) = |D/x|

nD

D

ta(q) = ((n+1)D-q)/x

e

X>0

X<0

q

ta(q) = |q-nD/x|

(n+1)D

Mapping Differential Equation Models into DEVS Integrator Models

DEVSinstantaneous

function

DEVS Integrator

d s1/dt s1f1x

d s2/dt s2f2

d sn/dt snfn

sx

sx

sx

...

d s1 /dt s1f1x

d s2 /dt s2f2

d sn /dt snfn

sx

sx

sx

...

DEVSSDEVS

DEVS

F

F

F

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)(tf

t

quantum

Number of crossings = Activity/quantum

Activity – a characteristic of continuous models

dttfdq

)(

Activity = |f(t1) – f(t0)|

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DEVS Efficiency Advantage where Activity is Heterogeneous in Time and Space

Time Period

T

time stepsize

# time steps

=T/

tt

activityA

quantumq

# crossings=A/q

Potential Speed Up=

#time steps /# crossings

X

numberof

cells

diffusion

activity

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Activity as unifying continuous and discrete paradigms

Heterogeneous activity in

time and space

Quantization allows DEVS to naturally focus computing resources on high activity regions

DEVS represents all decision making and continuous dynamic components in the

scene

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Modeling and Simulation as a Bridging Discipline (3)

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

Discrete Systems• Digital• Computer Science• Algorithms

DEVS• Representation • Quantized Integration• Discrete Pulse Wave Approx

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Modeling and Simulation as a Bridging Discipline (4)

Computational Science• Numerical Methods• Supercomputing• MPI• PDEs

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

DEVS• Discrete Event Universality• DEVS Simulation Protocol• Representation of Cont Sys

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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