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Structure Control in Agent-based Simulation

Structure Control in Agent-based Simulation. Bernard P. Zeigler, Ph.D., Co-Director, Arizona Center for Integrative Modeling and Simulation www.acims.arizona.edu and Joint Interoperability Test Command Fort Huachuca, AZ 85613-7051. Outline. Agent and multi-agent based simulation

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Structure Control in Agent-based Simulation

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  1. Structure Control in Agent-based Simulation Bernard P. Zeigler, Ph.D.,Co-Director,Arizona Center for Integrative Modeling and Simulationwww.acims.arizona.eduandJoint Interoperability Test CommandFort Huachuca, AZ 85613-7051

  2. Outline • Agent and multi-agent based simulation • DEVS modeling and simulation • DEVS support of agents • Structure change control • Application to distributed opportunistic testing of complex defense collaborative agent systems • Some issues and implications

  3. References, Available fromwww.acims.arizona.edu • Theory of Modeling and Simulation, 2nd Edition, Academic Press, Bernard P. Zeigler, Herbert Praehofer , Tag Gon Kim ,2000 • Nutaro, J., Hammonds, P., "Combining the Model/View/Control Design Pattern with the DEVS Formalism to Achieve Rigor and Reusability in Distributed Simulation", • Zeigler, B. P., Fulton, D., Nutaro, J., Hammonds, P., "M&S Enabled Testing of Distributed Systems: Beyond Interoperability to Combat Effectiveness Assessment", 9th Annual Modeling and Simulation Workshop, Dec. 8-11, 2003, ITEA White Sands Chapter • Zeigler, B.P., Fulton, D., Hammonds, P., Nutoro., J., "Framework for M&S-Based System Development and Testing in Net-centric Environment", in ITEA Journal, Nov, 2005 • Using Discrete Event Modeling and Simulation to Automate Testing In a Net-Centric Environment, Bernard P. Zeigler, Eddie Mak, Phillip Hammonds, Dale Fulton, Dasia Benson,Kimberly Nunn,

  4. Agent-Based Simulation • some of the simulated entities are agents • explicitly represents specific behaviors of specific individuals • contrast with traditional macro-level aggregated representations • extends object-oriented simulation • facilitates simulation of group behavior in highly dynamic situations • allows study of "emergent behavior" • well-suited to populations of heterogeneous individuals • vehicles (and pedestrians) in traffic situations • actors in financial markets • consumer behavior • humans and machines in battle fields • people in crowds • animals and/or plants in eco-systems • artificial creatures in computer games

  5. Multi-agent Systems • A dynamic system might be described as a multi-agent system • E.g. in a bio cell, agents are used as a metaphor to describe and understand the dynamics within the cell • enzymes, DNA, and mRNA and repressors interact as autonomous reactive entities • Suited for parallel and/or distributed simulation

  6. structure change ability Spectrum of Agent Properties domains … goal management capability intention management capability domain knowledge belief management capability language skills agent model decision making abilities communication capabilities perception abilities manipulation skills mobility skills navigation skills

  7. Model Specification Simulation Services Network /Middleware Layered Architecture

  8. How is simulation software different from other software? • It represents the behavior of dynamic systems whose states are functionally dependent on time • Properly controlling the flow of time is critical • Simulation software may combine: • continuous (time-driven) and discrete (event-driven) processes • actual operating hardware and software representations • wall clock and {faster/slower} than real time advance

  9. DEVS Background • DEVS = Discrete Event System Specification • Provides formal M&S framework: specification,simulation • 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

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

  11. Some Types of Models Represented in DEVS 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

  12. JAMES (Java-Based Agent Modeling Environment for Simulation) • DEVS-based framework facilitates experiments with agents under temporal and resource constraints • supports • endomorphy, i.e., models which contain internal models about themselves and their environment • variable structure models, i.e. models whose description entails the possibility to change their own structure and behavior • parallel distributed execution

  13. DEVS/RAPs • RAP (Reactive Action Package) • defines a tree of possible ways a task may be carried out with associated contingencies • elementary constructs are query and action (command) events • events are asynchronous messages generated internally or externally • RAPs compose hierarchically to provide highly flexible reactive decision making KIB (Knowledge Interchange Broker) handles synchronization, concurrency, and timing of interchanged messages

  14. Theater DSP/SBIRS Warning ABL AWACS JLENS F-15 THAAD TEL PATRIOT Link-16 specification MEADS AVENGER ATACMS SIS(MSCS) SIS(MSCS) AEGIS (CEP) Testing of interface standards is a focus area for automated simulation-based testing. Link-16 is required in all Joint and multi-national operations. The Joint Interoperability Test Command (JITC) has developed an automated test generation (ATC-Gen) methodology as its core technology for testing conformance of systems to Link-16 This methodology is fundamentally enabled by the DEVS formalized modeling and simulation approachSelected as the winner in the Cross-Function category for the 2004/2005 Department of Defense M&S Awards

  15. Test Driver SUT DEVS Simulator HLA HLA Network ATC-Gen Goals and Approach • Goals: • To increase the productivity and effectiveness of standards conformance testing (SCT) at Joint Interoperability Test Command (JITC) • To apply systems theory, modeling and simulation concepts, and current software technology to (semi-)automate portions of conformance testing Objective: Automate Testing Capture Specification as If-Then Rules in XML Analyze Rules to Extract I/O Behavior Synthesize DEVS Test Models Test Driver Executes Models to Induce Testable Behavior in System Under Test (SUT) Interact With SUT Over Middleware

  16. Transaction Level - example P.1.2 = Drop Track Transmit 1 Preparation 2 Processing 3 Modify C2 Record for TN Transmit Msg Rule Processing Constraints (Exception) Rules Validity checking Track Display Time outs Operator decisions Periodic Msg Other ConsequentProcessing Jumps (stimuli) to other Transactions of specification Stop Stop, Do Nothing, Alerts, Or jump to other Transaction Output from Input to system system DEVS t t t t 1 2 3 4 Discrete Event Nature of Link-16 Specification System Theory Provides Levels of Structure/Behavior

  17. ATC Gen Process Overview • Rule Capture in XML • Analyst interprets the requirements text to extract state variables and rules, where rules are written in the form: If P is true nowCondition Then do action A laterConsequence Unless Q occurs in the interimException • Dependency Analysis & Test Generation • DependencyAnalyzer (DA) determines the relationship between rules by identifying shared state variables • Test Model Generator converts Analyst defined test sequences to executable simulation models • Test Driver • Test Driver interacts with and connects to SUT via HLA or Simple J interfaces to perform conformance testing • Validated against legacy test tools

  18. Test Driver for Controlled Testing Coupled Test Model Component Test Model 1 Component Test Model2 Component Test Model 3 Jx1,data1 Jx2,data2 Jx3,data3 Jx1,data1 Jx2,data2 Jx3,data3 Jx1,data1 Jx2,data2 Jx3,data3 Jx4,data4 Jx4,data4 Jx4,data4 Middleware SUT

  19. Test Model • holdSend(Jx1,data1,t1) • holdSend (Jx2,data2,t2) • holdSend (Jx3,data3,t3) • waitReceive(Jx4,data4) Jx1,data1 Jx2,data2 Jx3,data3 Jx4,data4 t1 t2 t3 t4 time receiveAndProcess(Jx1,data1) receiveAndProcess(Jx2,data2) receiveAndProcess(Jx3,data3) transmit(Jx4,data4) SUT Model Test Model Generation for Controlled Testing Mirroring (flipping) the transactions of a SUT model (system model behavior selected as a test case) allows automated creation of a test model

  20. Multiplatform Distributed Simulation - Opportunistic testing Platform (System, Component) Platform (System, Component) Platform (System, Component) Observer Observer Observer Test Coordinator Distributed Observers look for opportunities to test

  21. Test Manager for Opportunistic Testing • Replace Test Models by Test Detectors • Deploy Test Detectors in parallel, fed by the Observer • Test Detector activates a test when its conditions are met • Test results are sent to a Collector for further processing Test Manager Jx1,data1 Jx2,data2 Jx3,data3Jx4,data4 Test Detector 1 Results Collector SUO Observer Test Detector 2 Other Federates Test Detector 3

  22. Test Detector • processDetect(Jx1,data1,t1) • processDetect(Jx2,data2,t2) • processDetect(Jx3,data3,t3) • waitReceive(Jx4,data4) Jx1,data1 Jx2,data2 Jx3,data3 Jx4,data4 t1 t2 t3 t4 time receiveAndProcess(Jx1,data1) receiveAndProcess(Jx2,data2) receiveAndProcess(Jx3,data3) transmit(Jx4,data4) SUO Test Detector Generation for Opportunistic Testing The Test Detector watches for the arrival of the given subsequence of messages to the SUO and then watches for the corresponding system output • Define a new primitive, processDetect, that replaces holdSend • Test Detector • Tries to match the initial subsequence of messages received by the SUO • When the initial subsequence is successfully matched, it enables waitReceive (or waitNotReceive) to complete the test

  23. Problem with Fixed Set of Test Detectors • after a test detector has been started up, a message may arrive that requires it to be re-initialized • Parallel search and processing required by fixed presence of multiple test detectors under the test manager may limit the processing and/or number of monitor points • does not allow for changing from one test focus to another in real-time, e.g. going from format testing to correlation testing once format the first has been satisfied Solution • on-demand inclusion of test detector instances • remove detector when known to be “finished” • employ DEVS variable structure capabilities • requires intelligence to decide inclusion and removal

  24. Dynamic Inclusion/Removal of Test Detectors Test Manager Active Test Suite Test Control removeAncestorBrotherOf(“TestControl"); message arrives test detector subcomponent removes its enclosing test detector when test case result is known (either pass or fail) add induced test detectors into test set addModel(‘test detector”); addCoupling2(" Test Manager ",“Jmessage",“test detector", “Jmessage");

  25. Example: Joint Close Air Support (JCAS) Scenario Natural Language Specification JTAC works with ODA! JTAC is supported by a Predator! JTAC requests ImmediateCAS to AWACS ! AWACS passes requestImmediateCAS to CAOC! CAOC assigns USMCAircraft to JTAC! CAOC sends readyOrder to USMCAircraft ! USMCAircraft sends sitBriefRequest to AWACS ! AWACS sends sitBrief to USMCAircraft ! USMCAircraft sends requestForTAC to JTAC ! JTAC sends TACCommand to USMCAircraft ! USMCAircraft sends deconflictRequest to UAV! USMCAircraft gets targetLocation from UAV!!

  26. AWACS Opportunistic Testing in JCAS CAS Model with AWACS observation Test Control Initially empty Test Suite

  27. AWACS Opportunistic Testing in JCAS (cont’d) Test Control observes CAS request message to AWACS Test Control adds appropriate Test Detector and connects it in to interface,

  28. AWACS Opportunistic Testing in JCAS (cont’d) First stage detector verifies request message receipt and prepares to start up second stage Test Control passes on start signal and request message

  29. AWACS Opportunistic Testing in JCAS (cont’d) First stage detector removes self from test suite second stage waits for expected response from AWACS to request

  30. AWACS Opportunistic Testing in JCAS (cont’d) Second stage observes correct AWACS response and removes itself and starts up second part

  31. AWACS Opportunistic Testing in JCAS (cont’d) At some later time, second part of Test Detector observes situation brief request message to AWACS First stage removes itself and starts up second stage

  32. AWACS Opportunistic Testing in JCAS (cont’d) Second stage observes situation brief output from AWACS thus passing test, It removes itself and enclosing Test Detector

  33. Structure Change Agent Architectures • Structure change: • Agents can add or remove other agents • Agents add or remove coupling between pairs of agents • Scope of effect: • anywhere in the hierarchical structure • within the children of parent or any ancestor • within their peer group • Scope of control • any agent can induce structure change • only specialized agents can induce structure change • Implementation issues: • within same processor • in distributed simulation • in real time

  34. Global Structural Change Examples

  35. Summary • Structure control is the ability of agents to induce structural change in themselves or others with the effects of enabling different behaviors under different circumstances. • It has been an under-considered aspect of intelligent/adaptive properties and the collective behaviors of such agents have yet to be explored. • Structure change is expressable in modeling and simulation environments based on Discrete Event Systems Specification (DEVS). • It supports opportunistic testing of complex defense collaborative agent systems. • Implications for modeling local and global structure transitions in a variety of disciplinary guises were suggested

  36. which agent capabilities are included or emphasized should depend on questions asked environment must be sufficiently rich to challenge selected agent capabilities environment agent-environment interaction agent embedded in environment agent to agent interaction

  37. domains … goal management capability intention management capability domain knowledge belief management capability language skills agent model decision making abilities communication capabilities perception abilities manipulation skills mobility skills navigation skills

  38. interactions goal management capability intention management capability belief management capability domain knowledge domains … language skills decision making abilities communication capabilities perception abilities manipulation skills mobility skills navigation skills

  39. down selection SIAP agent SACHEM agent

  40. integration/organization belief management capability intention management capability domain knowledge domains … goal management capability decision making abilities perception abilities language skills mobility skills navigation skills manipulation skills communication capabilities

  41. Accounting for crashes • vehicle/car-following conditions for crashes • weather conditions • driver perception/mental state

  42. On foot evacuations • information needed • daytime location of poplulation • children in school • pets • stay or leave

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