Bernard P. Zeigler Professor, ECE Arizona Center for Integrative Modeling and Simulation

1. DEVS Today: Recent Advances in Discrete Event - Based Information Technology Bernard P. Zeigler Professor, ECE Arizona Center for Integrative Modeling and Simulation University of Arizona Tucson www.acims.arizona.edu Keynote Talk to Majestic

2. Outline • Framework for M&S • Discrete Event Processing • DEVS Formalism • Implications for Current Practice • Application Examples • M&S as a Bridge Discipline

3. modeling relation simulation relation Framework for M&S: Entities and Relations Experimental Frame Device for executing model Real World Simulator Data: Input/output relation pairs Experimental frame specifies conditions under which the system is experimented with and observed Model Each entity is formalized as a Mathematical Dynamic System Each relation is represented by a homomorphism or other equivalence Structure for generating behavior claimed to represent real world

4. x x 0 1 Discrete Event Time Segments X t1 t0 t2 S e y0 Y

5. DEVS Background • DEVS = Discrete Event System Specification • Based on formal M&S framework • Derived from mathematical dynamical system theory • Supports hierarchical, modular composition • Object oriented implementation • Supports discrete and continuous paradigms • Exploits efficient parallel and distributed simulation techniques

6. DEVS Hierarchical Modular Composition Atomic: lowest level model, contains structural dynamics -- model level modularity Coupled: composed of one or more atomic and/or coupled models + coupling Hierarchical construction

7. DEVS Theoretical Properties • Closure Under Coupling • Universality for Discrete Event Systems • Representation of Continuous Systems • quantization integrator approximation • pulse representation of wave equations • Simulator Correctness, Efficiency

8. DEVS Expressability Coupled Models Atomic Models Partial Differential Equations can be components in a coupled model Ordinary Differential Equation Models Processing/ Queuing/ Coordinating Networks, Collaborations Physical Space Spiking Neuron Networks Spiking Neuron Models Processing Networks Petri Net Models n-Dim Cell Space Discrete Time/ StateChart Models Stochastic Models Cellular Automata Quantized Integrator Models Self Organized Criticality Models Fuzzy Logic Models Reactive Agent Models Multi Agent Systems

9. NW N NE S SE W E SW Coupled model structure Cell Space Ignite Wind Water Potential neighbor cells to ignite by fire from center cell.

10. Fire suppressant unburned_wet unburned Ignition and (fireline intensity Burning > Threshold) delay = 0 burned burning Fire suppressant and fire fighting rule satisfied Fire suppressant delay = 0 burned_wet burning_wet Atomic model structure Forest Cell State Transitions Fireline Intensity FI input input input Make a transition Make a transition Make a transition elapsed elapsed time time Time advance Time advance Phase “unburned” If (FI > Threshold) Phase “burning” holdIn (“burning”, else Compute new spread ( using passiva teIn( “Unburned)” Rothermel’s eq) Compute remaining distance to reach center of neighbor cell Compute time delays

11. Cell Space Display Forest Cell Igniter Display Average Rate Wind of Spread & Flow Direction Model Display Transducer Active Cells Fire Vs. Fighting Total Cells Model Display Other Stats Experimentation experimental frame Cell Space Ignite Wind Water

12. wind across valley floor experiments

13. water meets fire experiment

14. M&S Framework Implications for Current Practice • Separate Models From Simulators • Separate Models From Experimental Frames • Use the DEVS Formalism for Developing Models, Experimental Frames, and Simulators • Experimental Frames Support Defense Certification Testing • Maintain Repositories of Reusable Models and Frames

15. Separate Models From Simulators • Models are goal oriented abstractions of reality. • Simulators are the computational engines that drive the models to obtain results. Currently… Simulation software tends to encapsulate models and simulators in tightly coupled packages. In the M&S-Framework-based approach.. • Models and Simulators are treated as distinct entities with their own software representations. • There are simulators for different kinds of models that can be selected according to the needs of the simulation, • For example, a simulator might be chosen for its efficiency on a single host, or for its ability to execute the model on multiple hosts (distributed simulation)

16. Separate Models From Experimental Frames • Experimental Frames are specifications of the experimentation to be done on a model • Frames represent the objectives of the experimenter, tester, or analyst Currently… Simulation software tends to encapsulate models, simulators and experimental frames into tightly coupled packages. In the M&S-Framework-based approach.. • Models and Experimental Frames are treated as distinct entities with their own software representations. • Since the experimental frames appropriate to a model are distinctly identified, it is easier for potential users of a model to uncover the objectives and assumptions that went into its creation.

17. Use the DEVS Formalism for Developing Models, Experimental Frames, and Simulators • The DEVS formalism enables users to develop models separately from experimental frames . • Models and frames can then be coupled together and given to an appropriate simulator to execute. Currently… Programming languages such as Fortran, C, C++ or Java are used to develop software packages of strongly coupled models, frames and simulators. In the M&S-Framework-based approach.. • The DEVS formalism Is employed for all simulation software development. • DEVS simulators are employed to perform single host, distributed and heterogeneous real-time execution as needed. • DEVS simulators exist that run over various middleware such as MPI,HLA, CORBA,P2P, and MOM.

18. Maintain Repositories of Reusable Models and Frames • Models and Experimental Frames can be stored in organized repositories to support reuse under well specified conditions Currently… There are relatively few examples of storing previously developed simulation infrastructure commodities in such a way that they can be easily adapted to developing interoperability test requirements In the M&S-Framework-based approach.. • Repositories of models and frames are created and maintained. • Such repositories foster reuse of existing models and frames to serve as components for constructing new ones. • When new models or frames are developed they are deposited in the repositories with appropriate information to enable their reuse with high confidence of success.

19. Managed Modeling in Lockheed’s “System of Systems” M&S Environment • DEVS (Discrete Event Modeling Formalism) • Separates Model and Simulators • Defines Couple Models and Atomic Models • Modularized via Ports and Defined Events • SES (System Entity Structure) • Provides a well defined structure for model reuse • Maintains: kind-of, part-of, multiplicity relationships • Supports constraints on model compatibility • Architecture based on SES/DEVS supports component model reuse evolved during last decade

20. Component Reusability in Lockheed’s DEVS M&S Environment

21. DEVS framework for knowledge based control of steel production • Sachem = large-scale real-time monitor/diagnose control system for blast furnace operation • Usinor -- world’s largest producer of steel products, Sachem saves it millions of euros annually • Problems for conventional control and AI: • Experts’ perception knowledge is implicit, concerns dynamic physical processes • Difficult to model the reasoning of a control process expert. • Lack of mathematical models for blast furnace dynamics • Solution: • time-based perception and discrete event processing for dealing with complex dynamical systems

22. DEVS framework for knowledge based control of steel production (cont’d) quanti zation signal events signal pheno mena process pheno mena • Large Scale: • Conceptual model contains 25,000 objects for 33 goals, 27 tasks,etc. • Approximately 400,000 lines of code. • 14 man-years: 6 knowledge engineers and 12 experts • One advantage of DEVS is compactness: 50,000 reduction in data volume Effective analysis and control of the behavior of blast furnaces at high resolution

23. SimEnv Terrain Physics Dynamic Control Agents SimMan University of New Mexico Virtual Lab for Autonomous Agents V-Lab developed on top of DEVSJAVA includes a simulation environment for robotic agents with physics, terrain and dynamics. It extends DEVS to provide a layer for specifying intelligent automation and soft computing algorithms (IDEVS). IDEVS SimEnv DEVS Simulator Middleware (HLA,CORBA,JMS) Computer Network V-Lab-a virtual laboratory for autonomous agents-SLA-based learning controllersEl-Osery, A.I.; Burge, J.; Jamshidi, M.; Saba, A.; Fathi, M.; Akbarzadeh-T, M.-R.;Systems, Man and Cybernetics, Part B, IEEE Transactions on , Volume: 32 Issue: 6 , Dec. 2002 Page(s): 791 -803

24. s d s /dt s ò 1 1 x x f 1 s d s /dt s ò 2 2 x f 2 ... s d s /dt s ò n n x f n s d s /dt s F ò 1 1 DEVS x x f 1 S s d s /dt s DEVS ò 2 2 x f F 2 ... s d s /dt s ò DEVS F n n x f n Mapping Differential Equation Models into DEVS Integrator Models DEVS Integrator DEVS instantaneous function

25. Activity – a characteristic of continuous models Activity = |f(t1) – f(t0)| Number of crossings = Activity/quantum

26. DEVS Efficiency Advantage where Activity is Heterogeneous in Time and Space activity A # crossings =A/q Potential Speed Up = #time steps / # crossings quantum q X time step size number of cells # time steps =T/ Time Period T diffusion activity

27. Activity as unifying continuous and discrete paradigms DEVS represents all decision making and continuous dynamic components in the scene Heterogeneous activity in time and space Quantization allows DEVS to naturally focus computing resources on high activity regions

28. Representation • Quantized Integration • Discrete Pulse Wave Approx DEVS Modeling and Simulation as a Bridging Discipline (3) • Discrete Systems • Digital • Computer Science • Algorithms • Continuous Systems • Analog • Control theory • Linear/Non Linear • ODE/PDEs

29. Discrete Event Universality • DEVS Simulation Protocol • Representation of Cont Sys DEVS Modeling and Simulation as a Bridging Discipline (4) • PADS • Logical Process • Time Warp • Large Numbers • Network, Agent Apps • Computational Science • Numerical Methods • Supercomputing • MPI • PDEs

30. More Information • Zeigler, B.P., Praehofer, H., and Kim, T.G., Theory of Modeling and Simulation, 2nd Edition. Academic Press, 2000. • ACIMS : www.acims.arizona.edu DEVSJAVA downloadable software • Society for Modeling and Simulation, Intl. : www.scs.org • Simulation Journal, • new: Journal of Defense Modeling and Simulation