2. 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 www.acims.arizona.edu Ma Keynote Talk to Majestic

3. 2 Outline Framework for M&S Discrete Event Processing DEVS Formalism Implications for Current Practice Application Examples M&S as a Bridge Discipline

4. 3 Framework for M&S: Entities and Relations Real World Simulator modeling relation simulation relation 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 Device for executing model Model Experimental frame specifies conditions under which the system is experimented with and observed Experimental Frame Data: Input/output relation pairs

5. 4 x 0 x 1 X S Y y0y0 e t0t0 t1t1 t2t2 Discrete Event Time Segments

6. 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 DEVS Background

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

8. 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

9. 8 Atomic Models Ordinary Differential Equation Models Spiking Neuron Models Coupled Models Petri Net Models Cellular Automata n-Dim Cell Space Partial Differential Equations Self Organized Criticality Models Processing/ Queuing/ Coordinating Processing Networks Networks, Collaborations Physical Space DEVS Expressability can be components in a coupled model Multi Agent Systems Discrete Time/ StateChart Models Quantized Integrator Models Spiking Neuron Networks Stochasti c Models Reactive Agent Models Fuzzy Logic Models

10. 9 Cell Space Wind Water Ignite Coupled model structure N E NENW W SW SSE Potential neighbor cells to ignite by fire from center cell.

11. 10 unburned unburned_wet burning burned_wet burning_wet burned Fire suppressant Burning delay = 0 Ignition and (fireline intensity > Threshold) Fire suppressant delay = 0 Fire suppressant and fire fighting rule satisfied Forest Cell State Transitions Atomic model structure Time advance input Make a transition elapsed time Time advance input Make a transition elapsed time Phase “unburned” If (FI > Threshold) holdIn (“burning”, else passivateIn( “Unburned)” Compute new spread ( using Rothermel’s eq) Compute remaining distance to reach center of neighbor cell Compute time delays Fireline Intensity FI Phase “burning”

12. 11 Experimentation Wind Flow Model Fire Fighting Model Forest Cell Igniter Cell Space Display Transducer Display Average Rate of Spread & Direction Display Active Cells Vs. Total Cells Display Other Stats Cell Space Wind Water Ignite experimental frame

13. 12 wind across valley floor experiments

14. 13 water meets fire experiment

15. 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

16. 15 Separate Models From Simulators Models are goal oriented abstractions of reality. Simulators are the computational engines that drive the models to obtain results. 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) Currently… Simulation software tends to encapsulate models and simulators in tightly coupled packages.

17. 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 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. Currently… Simulation software tends to encapsulate models, simulators and experimental frames into tightly coupled packages.

18. 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. 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. Currently… Programming languages such as Fortran, C, C++ or Java are used to develop software packages of strongly coupled models, frames and simulators.

19. 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 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. 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

20. 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

21. 20 Project Model Critical Mobile Target Global Positionin g System III Arsen al Ship Coast Guard Deep Water Space Operatio ns Vehicle Commo n Aero Vehicle Joint Compos ite Tracking Network Integrat ed System Center Space Based Laser Space Based Discrimi nation Missile Defense (Theater / National) RAD xxxxxxx IR xxxxxxx MIS xxxxx LAS xxxx Comm xxxxxx CC xxx Earth xxxxx WC xx Component Reusability in Lockheed’s DEVS M&S Environment

22. 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

23. 22 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 DEVS framework for knowledge based control of steel production (cont’d)

24. 23 University of New Mexico Virtual Lab for Autonomous Agents V-Lab-a virtual laboratory for autonomous agents-SLA-based learning controllers El-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 Physics Terrain Dynamic SimEnv ControlAgentsSimMan Computer Network Middleware (HLA,CORBA,JMS) DEVS Simulator IDEVSSimEnv 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).

25. 24 Mapping Differential Equation Models into DEVS Integrator Models DEVS instantaneous function DEVS Integrator  d s 1 /dt s 1 f 1 x  d s 2 /dt s 2 f 2  d s n /dt s n f n s x s x s x...  d s 1 /dt s 1 f 1 x  d s 2 /dt s 2 f 2  d s n /dt s n f n s x s x s x... DEVS S F F F

26. 25 Number of crossings = Activity/quantum Activity – a characteristic of continuous models Activity = |f(t1) – f(t0)|

27. 26 DEVS Efficiency Advantage where Activity is Heterogeneous in Time and Space Time Period T time step size # time steps =T/ activity A quantum q # crossings =A/q Potential Speed Up = #time steps / # crossings X number of cells diffusion activity

28. 27 Activity as unifying continuous and discrete paradigms Heterogeneous activity in time and space Quantization allows DEVS to naturally focus computing resources on high activity regions DEVS represents all decision making and continuous dynamic components in the scene

29. 28 Modeling and Simulation as a Bridging Discipline (3) Continuous Systems Analog Control theory Linear/Non Linear ODE/PDEs Discrete Systems Digital Computer Science Algorithms DEVS Representation Quantized Integration Discrete Pulse Wave Approx

30. 29 Modeling and Simulation as a Bridging Discipline (4) Computational Science Numerical Methods Supercomputing MPI PDEs PADS Logical Process Time Warp Large Numbers Network, Agent Apps DEVS Discrete Event Universality DEVS Simulation Protocol Representation of Cont Sys

31. 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 softwarewww.acims.arizona.edu Society for Modeling and Simulation, Intl. : www.scs.org www.scs.org –Simulation Journal, –new: Journal of Defense Modeling and Simulation