Modeling and Simulation Methodology: The Challenge of Complex Endeavors

1. AI and Computing in Countering Terrorism INFORMS General Meeting Oct 13. 2008 Modeling and Simulation Methodology: The Challenge of Complex Endeavors Bernard Zeigler Arizona Center for Integrative Modeling and Simulation, University of Arizona, Tucson, AZ zeigler @ece.arizona.edu

2. Outline • What are Complex Endeavors? • We need adequate models of • humans • human-human interactions • What such models might be based on • Complex Endeavors as Systems of Systems • M&S Environment to Support SoS • Levels of Interoperability • SOA-based Integration and Testing of SoS

3. Marvin Minsky, The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind, Simon Schuster Richard E. (Dick) Hayes, Complex Endeavors as Challenges to the Modeling and Simulation Community, Military Modeling and Simulation Conference, Singapore Suiping Zhou, Human Behavior Modeling and Simulation For Military Operations, Military Modeling and Simulation Conference, Singapore

4. Complex Endeavors (Richard Hayes) • Formed when a large number of disparate entities form an association for a limited time to achieve a shared objective • No single leader or commander • Neither unity of purpose nor unity of command • Composed of independent entities Different traditions, cultures, goals, priorities, and processes • Interdependence • No single actor is capable of achieving its relevant goals independently • Actors bring different expertise and resources to the endeavor • Increasing need for international peace operations • information technology enables collaboration • multinational, interagency, governmental, non-governmental organizations, private industry, and local authorities

5. Complex Endeavors are characterized by Human-Human Interactions • Perceptions of actors about others • trust • competence • cross-cultural biases • Interoperability: share • information and knowledge • awareness (situation characterization) • understanding (cause and effect, temporal dynamics) • Collaboration about purposes, decisions, planning, and execution • coalitions without common doctrine • involving a variety of actors (e.g. Tsunami, Katrina relief) r

6. Limitations of Current Models and Reuse Models • Classic Rule Based and Algorithmic Models -- ignore soft factors • Human in the Loop Models —generalization limited to the types of people who participate • Simulation Models – Systems Dynamics, Agent-Based, etc., difficult for a policy or decision maker to comprehend, must have faith in black box • Problems in Reuse: • must know the original purposes and assumptions (Experimental frame) • models operate at different levels of abstraction – they cannot communicate • with each other • built in biases of developers, new forms e.g., cultural biases

7. Behavior Modeling Principles (Suiping Zhou) • Humans are social animals. The social aspect and the animal aspect of a human being are inhibitory to each other. • Behavior is largely determined by experiences rather than by complex decision rules. • Behavior is greatly affected by social context, family, friends, colleagues, etc • Human’s decision-making process consists of multiple layers of micro-level/macro-level interactions. • Decision making and perception are heavily influenced by emotion and culture

8. Layered Model of Mind (Marvin Minsky) Values, Censors, Ideals, Taboos Self-Conscious Reflection Self-Reflective Thinking Multiple, Concurrent Ways to think (Learning Processes) Reflective Thinking Deliberative Thinking Learned Reactions Learned Reactions Innate, Instinctive, Urges, Drives • We are born with many mental resources. • We learn more from interacting with others. • Emotions are different Ways to Think. • We learn to think about our recent thoughts. • We learn to think on multiple levels. • We accumulate huge stores of commonsense knowledge. • We switch among different Ways to Think. • We find multiple ways to represent things. • We build multiple models of ourselves.

9. Federations of Models • No single model or approach to modeling will be adequate to meet the needs for validity, reliability, and scalability. • Federations of models will be needed for different: • Levels of Analysis • Functions (Communications, Logistics, Decision Making, etc.) • Models in Federations should: • Be developed and tested together • Be modular and inform one another • Be based on compatible underlying assumptions and parameters • Be transparent

10. Interoperation vs Integration* Interoperation of components • participants remain autonomous and independent • loosely coupled • interaction rules are soft coded • local data vocabularies persist • share information via mediation Integration of components • participants are assimilated into whole, losing autonomy and independence • tightly coupled • interaction rules are hard coded • global data vocabulary adopted • share information conforming to strict standards reusability composability efficiency NOT Polar Opposites! * adapted from: J.T. Pollock, R. Hodgson, “Adaptive Information”, Wiley-Interscience, 2004

11. Linguistic Levels of Interoperability

12. Fundamental Research in M&S • Discrete Event System Specification (DEVS ) • Provides sound M&S framework • Derived from Mathematical dynamical system theory • Supports hierarchical, modular composition • System Entity Structure: ontology framework for M&S • Distributed simulation, web-based, SOA-based • Linguistic levels of interoperability (syntactic, semantic, pragmatic) • M&S Simulation interoperability standards

13. Fundamental Research in M&S (Cont’d) agents interactions Discrete-event, Models landscape Knowledge Interchange Broker Discrete-time, Cellular Automata Models Heterogeneous-Formalism Modeling NSF ERE Biocomplexity in the Environment program • Knowledge Interchange Broker (KIB) provides its own distinct formalism and realization • Separately accounts for domain-neutral and domain-specific modeling • Removes the need for composed models to have detailed knowledge of each other Design of Adaptive Service-based Software Systems with Security and Multiple QoS Requirements NSF Science of Design Program • Develop a SOA-based DEVS simulator to aid design and evaluation of flexible and configurable SOA-based software systems • support design of SOA systems able to adapt to changing tradeoffs among timeliness, throughput, accuracy, and security

14. Background: DEVS M&S Framework Experimental Frame Discrete Event Systems Specification (DEVS) • Based on mathematical formalism using system theoretic principles • Separation of Model, Simulator and Experimental Frame • Atomic and Coupled types • Hierarchical modular composition Source Simulator System Simulation Relation Modeling Relation Model message

15. Service Under Test Test Architecture DEVS Simulator DEVS Test Federation SOA DEVS Simulator Node SOAP-XML DEVS/SOA Federation Support Infrastructure Mission Thread SOA Service Discovery: UDDI Sevice Description: WSDL DEVS Observer Agent Packaging:XML Messaging:SOAP Communication: HTTP Live Test Player

16. DEVS Modeling and Simulation Infrastructure supports simultaneous testing at multiple levels Mission Thread Test Agents Control and Observe collaborations Pragmatic Level Tests DEVS acceptors alert higher layer agents of network conditions that invalidate test results Pragmatic Level agents inform Semantic Level agents of the objectives for health monitoring Semantic Level agents observe message exchanges between collaboration participants Semantic Level Tests Semantic Level agents activate probes at Syntactic Level network probes return statistics and alarms to DEVS transducers/ acceptors Syntactic Level Tests DEVS Modeling Language (DEVML) DEVS Simulator Services Middleware (SOAP, RMI etc) - Net-centric infrastructure

17. DEVS Simulation Concept • Specifies the abstract simulation engine that correctly simulates DEVS atomic and coupled models • Gives rise to a general protocol that has specific mechanisms for: • declaring who takes part in the simulation: • format for referencing federates (participants) • declaring how federates exchange information: • format for their message exchange patterns • executing an iterative cycle that • controls how time advances: • updating the clock based on next event times • determines when federates exchange messages: • the point in the cycle when all interchange takes place • determines when federates do internal state updating • the point in the cycle when next event times are collected • Note: • If the federates are DEVS compliant then the simulation is provably correct in the sense that the DEVS closure under coupling theorem guarantees a well-defined resulting structure and behavior. DEVS Simulator DEVS Protocol DEVS Model

18. Concept of DEVS Standard Single DEVS C++ Simulation processor Protocol Distributed Java Simulator DEVS DEVS DEVSML Model Real - Time Core Simulator Simulator Interface Interface Virtual - Time Non Simulator Other DEVS Representation

19. Define Requirements Capture Requirements Generate Atomic DEVS Models GenerateSystem Entity Structure Prune Entity Structure (PES) Integrated Development and Testing Methodology Interpret Structural Aspects Transform PES to hierarchical DEVS Models Simulate Interpret Behavioral Aspects CreateTest Models Simulation-Based Testing Insert Models into Test Platform Implement System

20. DEVS/SOA Infrastructure: Supports Deployment and Execution of DEVS Models on the Web WEB SERVICE CLIENT DEVS DEVS Agent Agent (Observer) ( Virtual User) WEB DEVSJAVA SERVICE CLIENT DEVS Modeling Language (DEVML) DEVS Simulator Services • Service Oriented Architecture (SOA) consists of various W3C standards • Client server framework • XML Message encapsulated in SOAP wrapper • Machine-to-machine interoperability over the network based on WSDL interface descriptions Middleware (SOAP, RMI etc) Net - centric infrastructure Run Example

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

22. DEVS/SOA Infrastructure for GIG Mission Thread Testing • MAJ Smith tasks Intell to reconnoiter objective area and provide threat estimate • 2. Posts taskings using Discovery and Storage 3. Intell Cell initiates high priority collection against objective, and collectors post raw output 4. Intell posts products via Discovery and Storage • MAJ Smith pulls • estimate from Storage NCES GIG/SOA

23. Observing Agent for Major Smith Observing Agent alerts other Agent Computes Time for Task, Measure Performance Observing Agent for Intell Cell notes time of posting sends time to other Agent DEVS/SOA Infrastructure for GIG Mission Thread Testing • Test agents are DEVS models and Experimental Frames • They are deployed to observe selected participant via their service invokations • MAJ Smith tasks Intell to reconnoiter objective area and provide threat estimate • 2. Posts taskings using Discovery and Storage 3. Intell Cell initiates high priority collection against objective, and collectors post raw output 4. Intell posts products via Discovery and Storage 5. Intell Cell issues alert via messaging • MAJ Smith pulls • estimate from Storage NCES GIG/SOA

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

25. Language of EncounterClassification of the Negotiation’s Primitives

26. Negotiation Scenario 1Language of Encounter Structure

27. Books and Web Links acims.arizona.edu Rtsync.com devsworld.org

28. Backup