Modeling and Simulation Methodology: The Challenge of Complex Endeavors

Bernard ZeiglerArizona Center for Integrative

Modeling and Simulation,University of Arizona,

Tucson, AZzeigler @ece.arizona.edu

AI and Computing in Countering Terrorism INFORMS General MeetingOct 13. 2008

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

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

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

Complex Endeavors are characterized by Human-Human Interactions

•Perceptions of actors about othersotrustocompetenceocross-cultural biases

•Interoperability: share oinformation and knowledgeoawareness (situation characterization)ounderstanding (cause and effect, temporal dynamics)

•Collaboration about purposes, decisions, planning, and execution

ocoalitions without common doctrine oinvolving a variety of actors (e.g. Tsunami, Katrina relief)

r

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

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

Layered Model of Mind (Marvin Minsky)

Self-Conscious Reflection

Self-Reflective Thinking

Reflective Thinking

Deliberative Thinking

Learned Reactions

Learned Reactions

Values, Censors, Ideals, Taboos

Innate, Instinctive, Urges, Drives

Multiple, Concurrent Ways to think(Learning Processes)

• 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.

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

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

* adapted from: J.T. Pollock, R. Hodgson, “Adaptive Information”, Wiley-Interscience, 2004

NOT Polar Opposites!

reusabilitycomposability

efficiency

syntactic

semantic

pragmatic

Linguistic Levels of Interoperability

LinguisticLevel

InteroperabilityDemonstrated 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.

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

Heterogeneous-Formalism Modeling agents

Discrete-event,Models

landscape

Discrete-time, Cellular Automata Models

Knowledge Interchange

Broker

interactions

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

NSF ERE Biocomplexity in the Environment program

NSF Science of Design Program

Design of Adaptive Service-based Software Systems with Security and Multiple QoS Requirements

• 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

QoS Adaptation

QoS Monitoring

SBS

Sim

ulat

ion

& Q

oS

me

asur

emen

ts

QoS Expectations

Adaptationcommands

ProduceEvents

Resources

ExtrageneousEvents

AffectQoS

ConsumeResources

Measure changes of resource states

[Adaptable Service Based Software system]

Fundamental Research in M&S (Cont’d)

Background: DEVS M&S Framework

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

Level Name System Specification at this level

4 Coupled Systems

System built from component systems with coupling recipe.

3 I/O System Structure

System with state and state transitions to generate the behavior.

2 I/O Function

Collection of input/output pairs constituting the allowed behavior partitioned according to initial state of the system. The collection of I/O functions is infinite in principle because typically, there are numerous states to start from and the inputs can be extended indefinitely.

1 I/O Behavior

Collection of input/output pairs constituting the allowed behavior of the system from an external Black Box view.

0 I/O Frame Input and output variables and ports together with allowed values.

Source System

Simulator

Model

Experimental Frame

SimulationRelation

ModelingRelation

message

DEVS/SOA Federation Support Infrastructure

LiveTest

Player

DEVS Observer

Agent

Service Discovery: UDDI

Sevice Description: WSDL

Packaging:XML

Messaging:SOAP

Communication: HTTP

SOA

Mission Thread

DEVS Modeling and Simulation Infrastructure supports simultaneous testing at multiple levels

Syntactic LevelTests

Semantic Level Tests

Pragmatic LevelTests

network probes return statistics and alarms to DEVS transducers/acceptors

Mission Thread Test Agents Control and Observe collaborations

Semantic Level agents activate probes at Syntactic Level

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

Middleware (SOAP, RMI etc)-

Net-centric infrastructure

DEVS Simulator Services

DEVS Modeling Language (DEVML)

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:o format for referencing federates (participants)

•declaring how federates exchange information: oformat for their message exchange patterns

•executing an iterative cycle that•controls how time advances:

oupdating the clock based on next event times•determines when federates exchange messages:

othe point in the cycle when all interchange takes place•determines when federates do internal state updating

othe 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.

DEVSSimulator

DEVSModel

DEVS Protocol

Concept of DEVS Standard

DEVS

CoreSimulatorInterface

Single

processor

Distributed

Simulator

Real- Time

Simulator

C++

Non

DEVS

DEVS

Model

Interface

Java

Other

Representation

DEVSSimulationProtocol

Virtual- Time

Simulator

DEVSML

Integrated Development and Testing Methodology

Define Requirements

Define Requirements

InterpretStructural Aspects

InterpretStructural Aspects

Capture Requirements

Capture Requirements

GenerateAtomic

DEVS Models

GenerateAtomic

DEVS Models

Generate System Entity

Structure

Generate System Entity

Structure

Prune Entity

Structure (PES)

Prune Entity

Structure (PES)

Transform PES to hierarchicalDEVS Models

Transform PES to hierarchicalDEVS Models

Create Test Models

Create Test Models

Insert Models into Test Platform

Insert Models into Test Platform

SimulateSimulate

InterpretBehavioral

Aspects

InterpretBehavioral

Aspects

ImplementSystem

ImplementSystem

Simulation-Based

Testing

Simulation-Based

Testing

DEVS/SOA Infrastructure: Supports Deployment and Execution of DEVS Models on the Web

WEBSERVICECLIENT

Middleware (SOAP, RMI etc)Net-centric infrastructure

DEVS Simulator Services

DEVS Modeling Language (DEVML)

DEVSJAVA

DEVSAgent

( Virtual User)

DEVSAgent

(Observer)

WEBSERVICECLIENT

Run Example

• 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

Search

find_xxxPost

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

DEVS/SOA Infrastructure for GIG Mission Thread Testing

1. MAJ Smith tasks Intell to reconnoiter objective area and provide threat estimate

2. Posts taskings using Discovery and Storage

6. MAJ Smith pulls estimate from Storage

3. Intell Cell initiates high priority collection against objective, and collectors post raw output

4. Intell posts products via Discovery and Storage

NCES GIG/SOA

DEVS/SOA Infrastructure for GIG Mission Thread Testing

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

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

NCES GIG/SOA

• Test agents are DEVS models and Experimental Frames

• They are deployed to observe selected participant via their service invokations

notes time of posting

Observing Agent alerts other Agent

Computes Time for Task,Measure Performance

sends time to other Agent

Negotiation Modeling Approach

Domain-dependentstructure

Domain-independentbehavior

FD-DEVS

SES

~ phases~ message types

message specializations

FD-DEVSMarket Place

ReceivemessageInterpret message

Sendmessage

Language of EncounterClassification of the Negotiation’s Primitives

Abort Initiators Reactors Completers informative

Terminate ContractQuery Offer Reject Busy

NotMet CapabilityQuery

CounterOffer Accept LinkEstablished

ItemRequest Decline BestProvidor

CapabilityStatement ProvidorsChosen

DomainName

Item

ItemCheckResult

• Negotiation Scenario 1Language of Encounter Structure

devsworld.org acims.arizona.edu Rtsync.com

Books and Web Links

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