1. M&S Ontology Framework for Complex System Development and Testing Bernard P. Zeigler Arizona Center for Integrative Modeling and Simulation University of Arizona March 2008

2. Modeling and Simulation (M&S) Framework Source System Simulator Model Experimental Frame Simulation Relation Modeling Relation

3. M&S Framework formulated within UML classes construction mechanisms relationships constraints create classes to satisfy use cases M&S Framework classes interpretation as software code (e.g. Java) Use Cases UML models simulators Source systems Experimental & Pragmatic Frames ontologies collect data store model run model construc t model store data simulate model sensor retri eve data retrieve model validate model memory control- simulator predictive -simulator model evaluator model generator real world model generated goal- generator action planning motor objectives M&S apply pragmatic frame apply experimen tal frame

4. DEVS – Formal Specification of a System A discrete event system specification (DEVS) is a structure M= where X is the set of input values, S is a set of states, Y is the set of output values,  int :S->S is the internal transition function,  ext, :Q  X->S is the external transition function,  con, :Q  X->S is the confluent transition function, ta: S->R + 0,∞ Where Q={(s,e)|s  S, 0  e  ta(s) } is the total state set, e is the time elapsed since last transition, :S->Y is the output function and R + 0,∞ is the set of positive reals with 0 and 

5. DEVS Hierarchical Modular Models DEVS Model Output port Input port DEVS Model message coupling SES Hierarchical Composition, Coupling and Variants are Represented in System Entity Structure System Entity Structure

6. 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 Some Types of Models Represented in DEVS can be components in a coupled model Multi Agent Systems Discrete Time/ StateChart Models Quantized Integrator Models Spiking Neuron Networks Stochastic Models Reactive Agent Models Fuzzy Logic Models

7. what defines a cake? -- distinguish cake from pie, bicycle,.. -- cover all varieties of acceptable cakes -- distinguish well cooked cakes from others e.g. Cake Ontology – Real world things Ontology: scheme for organizing data about a domain of interest testability – how to test whether the standard admits well-cooked cakes and no other things Possible world states allowed by ontology constraints Possible states allowed by ontology “slots”

8. System Entity Structure: Wine Ontology Example WhiteWineColor Regions Region MultiAsp Wine WineColorSpec RedWineColor RoseWineColor Region WineTasteSpec regionGrows regionGrowsMultiAsp wineCountryDec Region Wine wineCountry WineDec WineGrowing entity aspect specialization multiple aspect

9. SES-based Approach to Ontology Development Formal Model (Ontology) Implementation testing capability evaluate System Entity Structure (SES) offers framework for constructing model for domain of interest can be developed from formal model using SES to XML translation iterate

10. Birthday Cake instance3 Pruning Creates Submodels and Instances SES for Cake Use Aspects, Specializations, … and Pragmatic Frame to develop System Entity Structure SES for Birthday Cake Birthday Cake instance2 Birthday Cake instance1 Pruning based on pragmatic frame

11. pruning System Entity Structures Pruned Entity Structures transform Simulation Models (DEVS) XML Schemata XML instances ontology level implementation level DEVS/SES Ontology for M&S

12. System Entity Structure (SES) : SESBuilder

13. System Entity Structure/Model Base Repository: Support Automated DEVS Generation and Reuse Model construction SES Model Repository Experimental Frame prune and transform

14. Finite Deterministic DEVS : FD-DEVS FDDEVS = where incomingMessageSet, outgoingMessageSet, StateSet are finite sets TimeAdvanceTable: StateSet → R 0, ∞ + (the positive reals with zero and infinity) InternalTransitionTable: StateSet → StateSet ExternalTransitionTable: StateSet × incomingMessageSet → StateSet, OutputTable: StateSet → the set of subsets of outgoingMsgSet Natural Language For FDDEVS to start hold in PHASE for time SIGMA when in PHASE and receive MSG go to PHASE’ hold in PHASE for time SIGMA after PHASE then output MSG from PHASE go to PHASE’ Semantics defined by mapping into DEVS

15. FD-DEVS Example of Natural Language Spec: 1.to start passivate in waitForStart 2.when in waitForStart and receive StartAck go to sendFirst 3.hold in sendFirst for time 0.5 then output ReqForAck and go to sendSecond 4.hold in ackReceived for time Infinity 5.… waitFor Start send First send Second send Third Ack Received StartAck Ack 0.5 10 ∞ ∞ last Chance ReqForAck Ack The “right” abstraction of DEVS – retains important timing properties Amenable to analysis Supports automation Maps to DEVSJAVA Supplies a skelton that can be extended to full DEVS Simple XML expression

16. Overall Processing SES/XML Of DSR texts SES Pruned ES FDDEVS atomic model FDDEVS test models Augmented Test Models Automatedmanual

17. Automated Analysis: Based on State Input Pairs Enables Automated Test Model Generation listen Awaiting Adv OrderOfferProdFeasibilityFalse ProdFeasibilityTrue waitFor Confirm Confirm Dominant State-Input pair: listen,orderOffer Dominant State-Input pair: listen,orderOffer State-Input pair paths: 1= listen,OrderOffer 2 = listen, OrderOffer, awaitingAdv,ProdFeasibilityFalse 3= listen,OrderOffer awaitingAdv,ProdFeasibilityTrue 4= listen,OrderOffer awaitingAdv,ProdFeasibilityTrue waitForConfrm,Confirm Dominant State- Input pair paths: 2 4 Defined State-Input pairs: listen,orderOffer awaitingAdv,ProdFeasibilityFalse awaitingAdv, ProdFeasibilityTrue waitForConfrm,Confirm Defined State-Input pairs: listen,orderOffer awaitingAdv,ProdFeasibilityFalse awaitingAdv, ProdFeasibilityTrue waitForConfrm,Confirm Terminal State-Input Pairs: awaitingAdv, ProdFeasibilityFalse waitForConfrm, Confirm Terminal State-Input Pairs: awaitingAdv, ProdFeasibilityFalse waitForConfrm, Confirm State-Input Pairs mapped To Terminal State-Input pairs: listen,orderOffer -> awaitingAdv, ProdFeasibilityFalse awaitingAdv, ProdFeasibilityTrue -> waitForConfrm, Confirm

18. Generated Test Models in DEVSJAVA SimView AckCheck Send inputs Observe Outputs Trigger next in Sequence upon pass Observe Pass/Fail

19. Model Driven Architecture and Ontology Development UML ToolXMI XSLT Processor OWL MOF MetaObject Facility OUP Ontology UML Profile ODM Ontology Definition Metamodel Gašević, D., Djurić, D, Devedžić, V, Springer-Verlag, Berlin, 2006, In the SES/DEVS framework pruning is a critical behavior feature and there is no equivalent concept to pruning of SES instances in the UML DEVS is a formalism that intrinsically controls time advances in arbitrarily complex models. UML run time semantics explicitly state that UML does not dictate how time elapses between its events

20. Global Information Grid/Service Oriented Architecture Net-centric Enterprise Services (NCES) Net-Enabled Command & Control NCES: Secure, agile, robust, dependable, interoperable data-sharing environment for DOD where warfighter, business, and intelligence users share knowledge on a global network. This, in turn, facilitates information superiority, accelerates decision-making, effective operations and net-centric transformation.

21. Search find_xxx Post save_xxx Content/Service Catalogs/Registries Content/Service Consumer Content/Service Provider ServiceSOAP XML Schema WSDL Client Access (& Use) (Bind) XML Payload Simple Object Application Protocol Verification/ Validation relative to service Testing for Organization and Ontology quality Assessment of content for pragmatic, semantic, syntactic correctness Measurement of timeliness of information exchange Content discovery accuracy and effectiveness Requirements for Testing and Data Collection

22. Agents for Mission Thread–Based Testing SOAP 1.MAJ Smith tasks Intell to reconnoiter objective area and provide threat estimate 2. Posts taskings using Discovery and Storage 5. Intell Cell issues alert via messaging 6.MAJ Smith pulls estimate from Storage SOAP 3. Intell Cell initiates high priority collection against objective, and collectors post raw output 4. Intell posts products via Discovery and Storage Observing Agent for Major Smith Observing Agent for Intell Cell notes time of posting Computes Time for Task, Measure Performance sends time to other Agent Observing Agent alerts other Agent NCES GIG/SOA

23. Auto-Generation of Stand- alone Agent Operation

24. DEVS Agent Model & Experimental Frame (Step 2) DEVS models & GUI code The Structure of DEVS Hooks

25. Experimental Frame Output

26. Levels of System of System Interoperability Linguistic Level Interoperability Demonstrated if: Example Pragmatic – How information in message is used The receiver reacts to the message in a manner that the sender intends A commander’s order is obeyed by the troops in the field as the commander intended. (This assumes semantic interoperability.) Semantic – Shared understanding of meaning of messages The receiver assigns the same meaning as the sender did to the message. An order from a commander to multi-national participants in a coalition operation is understood in the same manner despite translation into different languages. Syntactic – Common rules governing composition and transmitting of messages The consumer is able to receive and parse the sender’s message A common network protocol (e.g., IPv4) ensures that all nodes on the network can send and receive data bit arrays while adhering to a prescribed format.

27. Mapping M&S Layers to Linguistic Levels Syntactic Level Semantic Level Pragmatic Level Execution Layer Abstract Simulators, Real time Execution, Animation Visualization Network Layer Distributed Grids, Service Oriented Architectures Semantic Web, Composition, Orchestration Ontologies, Formalisms, Model Dynamic Structure, Life Cycle Continuity, Model Abstraction Modeling Layer SES, DoDAF, Integrated System Development and Testing Design and Test Development Layer. Observers and Agents for Net-Centric Key Performance Parameters Experimental Frame Layer Collaboration Layer

28. Linguistic Levels in Testing Probe Layer Highest layer agents collaborate to control and observe mission thread executions Higher layer agents inform lower level agents of the objectives for health monitoring Middle layer alert higher layer agents of network conditions that invalidate test results Network probes return statistics and alarms to higher layer agents Middle layer agents activate probes at lower layer Information Exchange Layer End-to-end Mission Layer Syntactic Level Semantic Level Pragmatic Level

29. DEVS/SOA Infrastructure: Supports Deployment and Execution of DEVS Models on the Web WEB SERVICE CLIENT Middleware (SOAP, RMI etc) Net-centric infrastructure DEVS Simulator Services DEVS Modeling Language (DEVML) DEVSJAVA DEVS Agent ( Virtual User) DEVS Agent (Observer) WEB SERVICE CLIENT Run ExampleExample

30. Analysis-Based Network Data Extraction SES for Network Data Use Aspects, Specializations, … and Pragmatic Frame to develop System Entity Structure SES for Throughput Analysis pruning Network Data Collection SES for Protocol Analysis SES for Intrusion Detection

31. Negotiation Modeling Approach Domain-dependent structure Domain-independent behavior FD-DEVS SES ~ phases ~ message types message specializations storeSpec Surveillance Spec FD-DEVS Market Place Receive message Interpret message Send message

32. Applications Natural language and Ontologies capture of high level information technology systems requirements Automated generation of DEVS models for real-time net-centric IT systems testing Development of web service workflows using DEVS/SOA Network Traffic data capture, focused extraction, and model generation for exercising IT systems e.g., intrusion detection Negotiation Service Applications

33. devsworld.org acims.arizona.eduRtsync.com Books and Web Links