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 3

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 acquisition/development process 4

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 5

Background Wave Model for SoS Acquisition 6

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 7

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 8

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 9

Proposed Agent Based Model 10

SoS Environment External Factors/Variables: Changes in external environment at time T: External factors/variable at time T: 11

Proposed Agent Based Model 12

SoS Agent Behavior 1.Initiate SoS 2.Conduct SoS Analysis 3.Develop and Modify Architecture 4.Plan SoS update 5.Implement SoS architecture 6.Continue SoS analysis First Wave 13

Initiate SoS Simulation time: t Wave interval: Epoch Wave rhythm time: T T= Epoch. t SoS desired capabilities: Weighted value for SoS capability: SoS desired performance parameters: Initial SoS Measures: 14

Conduct SoS Feasibility Analysis 15

Genetic Algorithm 16 s1s1 s2s2 sisi snsn s 12 s 1j s 1n s 23 s n-1,n Chromosome representation – first Systems, then Interfaces Initial Population Mutations Crossover Fitness

SoS.M i Math Model Genetic Algorithm MATLAB SoS.B T (Fitness from Fuzzy Assessor) SoS.A 0 = max(Fitness.SoS.C g,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 20

Domain Specific Model 21 System Type Sub- System Ca p # 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 Num ber possi ble Sys tem Numb er FighterEO/IR RPAEO/IR FighterRadar JSTARSRadar TheatreExploit Control Station/ AOC C4I CONUSExploit LOS LinkComm BLOS Link Comm U-2EO/IR DSPIR * Table 2. SoS with 22 Systems: Capabilities, Costs, and Schedules

Chromosome and Domain Model 22 Feasibiity Performance Funding Flexibility Robustness Overall fitness

Fuzzy Assessments for ISR fitness 23

Fuzzy Evaluation Allows Both Non-Linearity and Simplicity 24 Plain Language Rule If 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 Update At time T: Adjust/Update SoS Measures Capability 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 25

Implement SoS Architecture Evaluate current SoS architecture against initial baseline Architecture 26

Proposed Agent Based Model 27

Individual System Behavior 1.Receive connectivity request from SoS agent 2.Evaluate request based on motivation – Pressure from outside – Capability – Desire to participate – Selfishness 3.Reply back to SoS agent 28

Evaluate SoS Request Individual System: System performance: System capability: Willingness to cooperate: Ability to cooperate: Receive request from SoS agent: Evaluate SoS request: 29

Reply back to SoS Agent If where system availability at time T= else time to cooperate: 30

Implementation Status 31 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 32

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

Questions 34