Understanding System of Systems Development Using an Agent-based

Wave Model

Presenters

Cihan H. Dagli, and Louis Pape

Missouri University of Science and Technology, Rolla, MO USA

Project Team • Principal Investigator: Dr. Cihan Dagli, Missouri University of

Science & Technology• Dr. Nil Ergin, Assistant Professor, Penn State• Dr. John Colombi, Assistant Professor, Air Force Institute of

Technology• Dr. George Rebovich, Director, Systems Engineering Practice

Office, MITRE• Dr. Kristin Giammarco, Associate Professor, Naval

Postgraduate School• Paulette Acheson, Khaled Haris, Louis Pape; PhD Students,

Missouri University of Science & Technology

Outline• SoS Engineering and Architecting Background• Research Objectives• Research Methodology

– Agent Based Model– Genetic Algorithm– Fuzzy Evaluation

• Agent-based Wave Model Status• Questions

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SoS Engineering and Architecting• “Acknowledged” SoS Characteristics

– Collaborate with existing systems/programs – Leverage individual functionalities/capabilities – “Minor” changes – cheap, fast; Existing missions remain!– Achieve new, hi-value SoS purpose/mission/capability

• Assumption: SoS participants exhibit nominal behavior– Deviation from nominal behavior leads to complications and

disturbances in system behavior and SoS success• Necessary to capture behavioral dimension of SoS architecting

to improve SoS acquisition – Not the normal DoDI-5000.02 acquisition/development process

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Acknowledged SoS• The SoS manager has a requirement for a new capability, not

currently available, but potentially available with “small” modifications to existing Systems; there may be “small” funding available for the SoS

• The component Systems are independently managed and funded– They have their own missions, requirements, and stakeholders

independent of the SoS– They may be in any stage of their life cycle– There are no guarantees that they will be able to deliver any part of

the capability they are asked to provide to the SoS

• Participation in the SoS may be desired, but infeasible

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Background • Wave Model for SoS Acquisition

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Research Objectives• Develop a Model of SoS acquisition based on the Wave

Process Model• Test the concept implementation on the DoD Intelligence,

Surveillance, and Reconnaissance (ISR) domain• Ultimate goal

– Explore the impact of individual system behavior on SoS development

• How do system characteristics, systems’ interactions, SoS initial requested capabilities, and other elements affect:

– Capabilities Actually Developed vs. Planned Capabilities – Duration of the SoS development

– Strategies for improving acquisition effectiveness• Examine decision framework• Test rules of engagement changes

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Case Study - ISR Mission /RPA SoS• Individual systems

– Remotely Piloted Aircraft– Fighter Aircraft, JSTARS, U-2– Datalinks (Link 16…)/ SATCOM…– Ground Control Station(s)…– Sensors (Wide Area Search, Electro-Optic, Radar)…– Weapon(s) – Exploitation Centers

• Target scenario– Gulf War Scud Launchers

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Research Methodology• Agent-based modeling

– Environment• Rules of engagement• Opportunities• Threats

– Agents• Autonomous • Internal behavior

– Interactions

• Binary SoS Architecture of system participation and interfaces• Genetic algorithm exploration of binary architecture “space”• Fuzzy evaluation of SoS architecture fitness

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Proposed Agent Based Model

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SoS Environment

External Factors/Variables:

Changes in external environment at time T:

External factors/variable at time T:

), , (0 threatsfundingSoSinchangesprioritiesNationalfE

T

TT EE 0

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Proposed Agent Based Model

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SoS Agent Behavior1. Initiate SoS2. Conduct SoS Analysis3. Develop and Modify Architecture4. Plan SoS update5. Implement SoS architecture6. Continue SoS analysis

First Wave

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Initiate SoSSimulation time: tWave interval: Epoch Wave rhythm time: TT= Epoch . t

SoS desired capabilities: Weighted value for SoS capability:

SoS desired performance parameters:

Initial SoS Measures:

),...,,(. 21 ni CCCCSoS

iiiiiinij wSoSaPSoSaCSoSaaMSoS . ,. ,. where][. 32130

),...,,(. 21 ni wwwwSoS

),...,,(. 21 ni PPPPSoS

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Conduct SoS Feasibility Analysis

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Genetic Algorithm

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s1 s2 si sn s12 s1j s1n s23 sn-1,n

Chromosome representation – first Systems, then Interfaces

Initial Population

Mutations

Crossover

Fitness

6

5

4

3.5

8

9

0 0 1 1 1 1 0 1 0 1 1 0 0 0 1 1 1 1 1 0 0 0 1 1 0 0 1 0 1 1 1 0 0 0 1 1

1 0 0 1 1 0 1 1 0 1 0 0 0 0 1 1 1 0 0 0 1 0 1 1 1 1 1 1 1 1 0 0 1 1 0 1

1 0 0 0 1 0 1 0 1 0 1 0 1 1 1 1 1 0 1 1 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0 1

0 0 1 1 1 1 0 1 0 1 1 0 0 0 1 1 1 1 1 0 0 0 1 1 1 0 1 0 1 1 1 0 0 0 1 1

1 0 0 1 1 0 1 1 1 1 0 0 0 0 1 1 1 0 0 0 1 0 1 1 1 1 1 1 1 1 0 0 1 1 0 1

1 0 0 0 1 0 1 0 1 0 1 0 1 1 0 0 0 0 0 1 0 1 1 1 0 1 1 0 1 0 0 0 1 0 0 1

SoS.Mi

Math Model

Genetic Algorithm

MATLAB

Population of Chromosomes

SoS.BT (Fitness from Fuzzy Assessor)

Highest Fitness Chromosome = Initial SoS Architecture

SoS.A0 = max(Fitness.SoS.Cg,n )

Best SoS Architecture

• The SoS meta-architecture is expressed as an optimization problem to find the best architecture through genetic algorithm methods

SoS Fuzzy Attributes• Performance

– Coverage, Prob of detection, Timeliness, etc

• Affordability– Development and Operations Costs vs budget

• Flexibility– Ability of SoS Manager to Develop Capabilities from

Multiple Systems

• Robustness– Minimize Capability Lost Through Loss of 1 Platform in

Operation

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Domain Specific Model

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System Type Sub-System

Cap #

Coverage sq mi/hr;

Band width Mb

Attack Speed, MPH or process time, sec

$ Develop $M/ epoch/ interface

$ Operate $K/hr per system

Time to Devel op, Epochs

Number possible

System Number

Fighter EO/IR 1 500 350 .2 10 1 3 1-3RPA EO/IR 1 2000 150 2 2 2 4 4-7Fighter Radar 2 3000 350 .7 10 2 3 8-10JSTARS Radar 2 10000 .1 18 0 1 11Theatre Exploit 4 5000 90 2 10 1 2 12-13

Control Station/ AOC

C4I 5 1 30 1 2 1 2 14-15

CONUS Exploit 4 25000 120 .2 0 0 1 16LOS Link Comm 3 .25 - .2 0 1 2 17-18

BLOS Link

Comm 3 2 - 0.5 3 0 2 19-20

U-2 EO/IR 1 50000 - 0 15 0 1 21DSP IR 1 100000*.0

1 1 1 0 1 22

Table 2. SoS with 22 Systems: Capabilities, Costs, and Schedules

Chromosome and Domain Model

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Table 1. Linear (in the top row) and upper triangular display of the SoS chromosome, with system deadline, funding, and performance

1 1 1 1 1 0 0 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 0 1 1 0 1 1 1 0 1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 System number

Interfaces -> 1 1 1 1 1 0 0 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 System participating =1

1 1 1 1 0 0 1 1 0 1 1 1 0 1 1 1 1 0 1 0 0 1 Syst in yellow row interface with this Syst(j)

1 0 1 0 0 1 1 0 1 1 1 1 1 1 1 0 1 1 0 1 2 fighter

1 1 0 0 1 1 0 0 1 1 0 1 1 1 1 1 1 0 1 3

1 0 0 0 1 0 1 1 0 0 1 1 1 1 1 1 0 0 4 RPA eoir

0 0 1 1 0 1 1 1 1 1 1 0 1 0 0 0 1 5

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7

1 0 1 1 1 0 1 0 1 1 1 1 0 1 8 fighter SAR

0 0 1 1 1 1 0 1 1 0 0 0 1 9

0 0 0 0 0 0 0 0 0 0 0 0 10

1 0 1 1 1 1 1 1 1 0 0 11 jstar

1 1 1 1 1 1 1 0 0 1 12 TheatreExp

1 1 1 1 1 1 1 0 0 13

1 1 1 1 1 0 0 1 14 control sta

0 1 0 1 1 0 0 15

1 1 1 1 0 1 16 CONUS

1 1 1 0 1 17 LOS

1 1 0 1 18

0 0 1 19 BLOS

0 0 20

0 21 U-2

D1 D2

D3

D4 D D D D D D D D D D D D D D D D D21 D22 Deadlines

F1 F2 F3 F4 F F F F F F F F F F F F F F F F F21 F22 Funding

P1 P2 P3 P4 P P P P P P P P P P P P P P P P P21 P22 Performance FITR EO/IR RPA FITR SAR Jstar

ThtrExp Ctrl Sta Con LOS BLOS U-2 DSP

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

• Feasibiity• Performance• Funding• Flexibility• Robustness• Overall fitness

Fuzzy Assessments for ISR fitness

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Fuzzy Evaluation Allows Both Non-Linearity and Simplicity

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Plain Language RuleIf ANY attribute is Unaccptable, then SoS is Unacceptable

If ALL the attributes are Exceeds, then the SoS is Exceeds

If ALL the attributes are Marginal, then the SoS is Unacceptable

If ALL the attributes are Acceptable, then the SoS is Exceeds

If (Performance AND Affordability ) are Exceeds, but (Dev. Flexibility and Robustness) are Marginal, then the SoS is Acceptable

If ALL attributes EXCEPT ONE are Marginal, then the SoS is still Marginal

Plan SoS UpdateAt time T:• Adjust/Update SoS MeasuresCapability update factor:

Performance update factor:

SoS Measures update factor:

At T=0

SoS Measures at time T:

• Adjust wave rhythm interval:

• Adjust budget/schedule for allocated capabilities

),...,,(. 21 ni CCCCSoS

),...,,(. 21 ni PPPPSoS

).,(. Tti GapSoSEfCSoS

).,(. Tti GapSoSEfPSoS

iiii

nijT

PSoSaCSoSa

aAlphaSoS

. and .

where][.

21

2

TT AlphaSoSMSoSMSoS ... 0

0. TAlphaSoS

).,( TT GapSoSEfEpoch

).,(.

).,(.

TTi

TTi

GapSoSEffSoS

GapSoSEfdSoS

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Implement SoS Architecture• Evaluate current SoS architecture against initial baseline

Architecture

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Proposed Agent Based Model

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Individual System Behavior1. Receive connectivity request from SoS agent2. Evaluate request based on motivation

– Pressure from outside– Capability– Desire to participate– “Selfishness”

3. Reply back to SoS agent

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Evaluate SoS RequestIndividual System: System performance:System capability:Willingness to cooperate:Ability to cooperate:Receive request from SoS agent:

Evaluate SoS request:

iSSystem.

ipSystem.

icSystem.

iswillingnesSystem.

iabilitySystem.

iRSoS.

).,.,.(. iiii RSoSabilitySystemswillingnesSystemfcoopSystem

cooperatenot if 0

cooperate if 1. icoopSystem

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Reply back to SoS AgentIf

where system availability at time T= elsetime to cooperate:

1. icoopSystem

).,.,.(. iiii avSystempSystemcSystemnInformatioSystem

ii dSoSttcooptimeSystem . where.

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).(. ii RSoSPavSystem

Implementation Status

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• ISR Domain model created• GA produces architecture chromosomes• Agent Based Model fuzzy evaluates chromosomes• System data interchange format for negotiations established

Next Steps• Integrate negotiation models for individual system

decisions• Explore rules of engagement impacts and update a

negotiation process for SoS agent• Ultimate goal

– Understand impact of individual system behaviors and environment on SoS development

• Capabilities Actually Developed vs. Planned– Strategies for improving acquisition effectiveness

• Decision framework• Rules of engagement

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AcknowledgmentThis material is based upon work supported, in whole or in part, by the U.S. Department of Defense through the Systems Engineering Research Center (SERC) under Contract H98230-08-D-0171. SERC is a federally funded University Affiliated Research Center managed by Stevens Institute of Technology.

Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the United States Department of Defense.

See Research Report for RT-37, http://www.sercuarc.org/projects

A related paper being presented at CSER 2013 ( http://cser13.gatech.edu/ ): A Fuzzy Evaluation Method For System Of Systems Meta-architectures. Louis Pape, Kristin Giammarco, John Colombi, Cihan Dagli, Nil Kilicay-Ergin , George Rebovich

Paulette Acheson, Cihan Dagli, Louis Pape, Nil Kilicay-Ergin, John Columbi, Khaled Haris. “Understanding System of Systems Development Using an Agent- Based Wave Model”, Procedia of Computer Science, Volume 12, Elsevier, Pages 21-30, 2012

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Questions

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