1. Previous experience n Background (Carleton / Ottawa U / Special ?) –Systems/Computer Engineering –Computer Science –Electronic/Electrical Engineering –Industrial/Mechanical Engineering –General Sciences: Mathematics, Chemistry, Physics, etc. –Natural Sciences/Medicine –Social Sciences –Other? n Experience in the area –Courses in Modelling and Simulation? –DEVS? (Basic/Advanced) –Parallel simulation (Basic/Advanced) –Programming languages for discrete-event models (Basic/Advanced) –None

2. Problem Solving Results Experiment Experimental Frame Entity Analysis of natural/artificial real systems. Experimentation.

3. Modeling of Natural Systems n Analytical methods (300+ years Newton-Leibniz).

4. The problem solving cycle

5. Analytical Modeling Equations Results Experiment Experimental Frame Entity Results Query Model's Exp. Frame Model n Analytical: –Based on reasoning –Symbolic –General solutions to existing systems

6. The problem analysis cycle

7. Problems with Analytical Modeling n Complexity: analytical solutions cannot be provided. –Impossible to define –Impossible to solve –Simplifications n Numerical Methods

8. Numerical Approximation Approximation Results Experiment Experimental Frame Entity Query Model's Exp. Frame Model Approximate Results Computed Query Computation Exp. Frame Compute

9. Artificial Systems Modeling n Complexity: analytical solutions cannot be provided. n Differential equations and approximations: inadequate tools

10. Modeling Artificial Systems G Y R G: 45s Y: 10s R: 55s

11. Automata Simulation Approximation Results Experiment Experimental Frame Entity Query Model's Exp. Frame Model Approximate Results Computed Query Computation Exp. Frame Compute G Y R

12. Along Came the Computer… n 1950’s: simulation –Particular solutions for a given problem –Controlled experimentation –Time compression n Mixed problems –Solving numerical methods more efficiently –Computing automata-based models –Conducting a large number of experiments in a controlled fashion at a low cost

13. Experiment Building a Simulator Program Results Experiment Experimental Frame Entity Results Simulator

14. Building a Simulator time = 0; State = Green; Repeat Forever { if (State == Green AND (time mod 110) == 45) State = Yellow; if (State == Yellow AND (time mod 110) == 55) State = Red; if (State == Red AND (time mod 110) ==110) State = Green, time = time + 5; } Automata Numerical Approximation t f(t) h

15. Single-use Program Approach n Reuse of simulation software in a different context? n Reuse of experiments carried out? n Changes in the model? n Updates in the model? n Where is the abstract model to use to organize our thoughts? n How do we validate the results? What if we find errors?

16. Discrete-Event Dynamic Systems

17. Modeling DEDS n How do we model the external sensory information? n If we need to combine this traffic light with others, how is the variable-timing behavior going to affect the combined automaton? n Which would be right timestep to be used? n What are the “differential equations” for this problem? n Lights for the whole city: explosion of states?

18. Experiment Building a Simulator Program Results Experiment Experimental Frame Entity Results Simulator

19. Discrete-Event M&S Based on programming languages (difficult to test, maintain, verify). Beginning ’70s: research on M&S methodologies. Improvement of development task. Focus in reuse, ease of modeling, development cost reductions.