Understanding System of Systems Development Using an Agent-based Wave Model

1. 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

2. 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

3. Outline • SoS Engineering and Architecting Background • Research Objectives • Research Methodology • Agent Based Model • Genetic Algorithm • Fuzzy Evaluation • Agent-based Wave Model Status • Questions

4. 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

5. 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

6. Background • Wave Model for SoS Acquisition

7. 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

8. 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

9. 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

10. Proposed Agent Based Model

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

12. Proposed Agent Based Model

13. FirstWave SoS Agent Behavior • Initiate SoS • Conduct SoS Analysis • Develop and Modify Architecture • Plan SoS update • Implement SoS architecture • Continue SoS analysis

14. 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:

15. Conduct SoS Feasibility Analysis

16. Genetic Algorithm Chromosome representation – first Systems, then Interfaces Fitness 6 5 4 3.5 8 9 Initial Population Mutations Crossover

17. SoS.Mi SoS.BT (Fitness from Fuzzy Assessor) Math Model Genetic Algorithm MATLAB SoS.A0 = max(Fitness.SoS.Cg,n )

18. Best SoS Architecture The SoS meta-architecture is expressed as an optimization problem to find the best architecture through genetic algorithm methods

20. 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

21. Domain Specific Model Table 2. SoS with 22 Systems: Capabilities, Costs, and Schedules

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

23. Fuzzy Assessments for ISR fitness

24. Fuzzy Evaluation Allows Both Non-Linearity and Simplicity

25. 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

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

27. Proposed Agent Based Model

28. Individual System Behavior • Receive connectivity request from SoS agent • Evaluate request based on motivation • Pressure from outside • Capability • Desire to participate • “Selfishness” • Reply back to SoS agent

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

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

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

32. 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

33. 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 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

34. Questions