1. 2003 Fall SIW The Process for Coercing Simulations Sarah Waziruddin, University of Virginia David Brogan, University of Virginia Paul Reynolds, University of Virginia

2. 2003 Fall SIW Motivation Facilitate the reuse process Simulation reuse is highly desirable Reuse may occur: Modify a simulation to achieve a different set of objectives Perhaps change resolutions Compose simulations to meet an arbitrary objective

3. 2003 Fall SIW User Composability Concept of Operations CONSTRUCTION AND EVALUATION Options for Composed Models Module Repositories USER SEARCH Issue: 1)Interoperability 2)Constraints “I have a need “I have a solution!” Selected modules REQUIREMENTS CAPTURE Issue: What should these modules contain Issue: How do requirements relate to module functionality (Translate user requirements into model requirements) Ernest Page, “Observations on the Complexity of Composable Simulation” In Proceedings of Winter Simulation Conference, December 1999 in Phoenix, AZ If interoperability of selected modules is an issue, coercion offers a potential solution.

4. 2003 Fall SIW The Goals of Coercion Increase efficiency of the reuse process Provide tools that lead to new insight about the simulated phenomenon Increase the practitioners' confidence in the model

5. 2003 Fall SIW P: The Coercion Process P Requirements Coerced Simulation Simulation to reuse Practitioners

6. 2003 Fall SIW An Example Bicyclist: Slower and More Accurate Model P Coerced Particle: Relatively Fast and Accurate Model Particle: Faster and Less Accurate Model Trajectory Bicyclist Particle

7. 2003 Fall SIW P: The Coercion Process P = [m*, o*]* Some Example Sequences: m, o, m, o o, o, m, m, o, m m o

8. 2003 Fall SIW P: The Coercion Process Explained Identify Flexible Elements Set Constraints Define Objective Function Select Optimization Technique Run Optimizer Identify Functionality to Modify Make Modification Done No Yes Successful Coercion? Run optimizer or modify? Evaluate Modify Optimize Identify Flexible Elements Set Constraints Define Objective Function Select Optimization Technique Run Optimizer Identify Functionality to Modify Make Modification Done No Yes o m Successful Coercion? Run optimizer or modify?

9. 2003 Fall SIW O: Optimization Step Need a human in the loop for the initial tasks Utilize optimization technology to automate coercion Identify Flexible Elements Set Constraints Define Objective Function Select Optimization Technique Run Optimizer

10. 2003 Fall SIW M: Modification Step Modify or augment the existing algorithm of the simulation Identify Functionality to Modify Make Modification

11. 2003 Fall SIW Evaluation The practitioner determines if coerced simulation meets requirement The initial simulation’s output is encoded as a behavior: S 0  B 0 The coerced simulation’s output is encoded as a behavior: S n  B n The requirement is encoded as a behavior, B target Successful Coercion?

12. 2003 Fall SIW Evaluation Function B target  B n An infinite B n can satisfy this relationship B target Bn B target Bn

13. 2003 Fall SIW Paths to Coercion S0S0 S0S0.. S0S0 SnBnSnBn SnSn SnSn..

14. 2003 Fall SIW Possible Scenarios O  O The optimizer fails to converge The optimizer gets stuck in a local minima Discontinuity, non- linear, large search space O  M When successive optimizations fails to yield successful coercion Modification may be needed to eliminate non-linearities, discontinuities, etc.

15. 2003 Fall SIW Possible Scenarios M  O Modification may lead to discovery of new flexible elements The optimal values for the flexible elements may change due to the modification M  M Optimization is too costly/impossible

16. 2003 Fall SIW An Example S 0 = f(x) = y = -0.25x + 0.5 Requirement = g(x) = y = sine(x) Define B target by specifying 16 sample points:

17. 2003 Fall SIW An Example

18. 2003 Fall SIW O  O  M  O  M  O First Iteration: Optimization Motivation: We want as much automation as possible Initial tasks: Identify flexible elements: The slope and y- intercept Set constraints: None Define the objective function: Choose an optimization technique: Linear regression

19. 2003 Fall SIW O  O  M  O  M  O

20. 2003 Fall SIW O  O  M  O  M  O Flexible elements: Slope and y-intercept have values of 0 Objective function: Value of 0 (no error) but B target has not been met Insight: Individual errors sum to 0 four have an error of zero, four have an error of, four have an error of, two have an error of one, and two have an error of negative one Next Iteration: Modify objective function

21. 2003 Fall SIW O  O  M  O  M  O Second Iteration: Optimization Motivation: Modify objective function to leverage automation Initial tasks: Identify flexible elements: The slope and y- intercept Set constraints: None Redefine the objective function: Choose an optimization technique: Linear regression

22. 2003 Fall SIW O  O  M  O  M  O

23. 2003 Fall SIW O  O  M  O  M  O Flexible elements: Slope and y-intercept have values of 0 Objective function: Value of Insight: Line fragments track sine curve better than a line Next Iteration: Modify the simulation to produce line fragments

24. 2003 Fall SIW O  O  M  O  M  O Third Iteration: Modification Functionality to modify: Produce line fragments that span a parameter space of π Fourth Iteration: Optimization Motivation: Find optimal values for flexible elements in modified algorithm Initial tasks: Same as the second iteration of optimization

25. 2003 Fall SIW O  O  M  O  M  O Line fragments do not match sample points defined by B target Objective function: Error value decreases from previous iteration Insight: Smaller line fragments track sine curve better Next Iteration: Modify the simulation to produce twice as many line fragments

26. 2003 Fall SIW O  O  M  O  M  O Fifth Iteration: Modification Functionality to modify: Produce line fragments that span a parameter space of π/2 Sixth Iteration: Optimization Motivation: Find optimal values for flexible elements in modified algorithm Initial tasks: Same as the second iteration of optimization

27. 2003 Fall SIW O  O  M  O  M  O

28. 2003 Fall SIW O  O  M  O  M  O Line fragments match sample points defined by B target Objective function: Error value is 0 Practitioner verifies results using visualization tools B target   B n ; Coercion is successful! Multiple sequences can succesfully coerce this simulation to B target

29. 2003 Fall SIW Tools available SimEx: a tool in production to aid practitioners to coerce a simulation Includes visualization, optimization and constraint specification modules

30. 2003 Fall SIW Ongoing Work Is there a best coercion sequence? Is there a best initial simulation? Is there a best coerced simulation? Tools: Maximize human interaction in the process Identify flexible elements Visualization tools Optimization tools

31. 2003 Fall SIW Conclusion Coercion Formally specified a semi-automated reuse process Greater insight on simulation code Greater insight on simulated phenomenon Insight leads to greater confidence in model

32. 2003 Fall SIW Acknowledgment Defense Modeling and Simulation Office Multi-Resolution Modeling group at the University of Virginia

33. 2003 Fall SIW Questions ?