mastering complexity extracting order from chaos 3rd annual san diego regional security conference...

Mastering Complexity

Extracting Order from Chaos

3rd Annual San Diego Regional Security Conference

Intelligent Integration

The Next Generation of Intelligent, Automated Systems

Jeffrey Wallace and Barbara Hannibal


September 14-15, 2010




About Us

• Unique Systems Engineering and Development– A distillation of over $2B in government R&D

• Partnered with OSD, JFCOM, “Team Orlando”, the 4 services, SRI, Academia, and others

• Solved world-class/grand challenge problems on F-35, CVN-21, healthcare IT, etc.

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Underlying Technology Thrusts

• “Nervous System” – component activation, integration, and interoperability

• Knowledge representation and reasoning– Conceptual graphs– Problem solving module integration

• Complex context representation along with reasoning integration and problem solving– Ontologies and other Semantic Web

technologies3




Problems• Little to no time for critical decisions

– Imperfect information, must adapt to “dirty” environments

• Interactions happening faster than humans can react– Volume of information, battlespace clutter, and

increasing speed: DE, hypersonics, UxVs

• Current Command and Control (C2) is not real-time and the only source of information

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Chinese Anti-Ship Missile

Example: Anti-Ship Missile Defense

Increase Overall System Speed and Effectiveness Simplify Installation and Maintenance

SBX

DDG

DF-21

UAS

CIWS

M-Ship




Summary• Capture needs through application of novel

techniques• Creation of complex, realistic, and scalable networks

of component inter-relationships• Distribution of autonomous controls and monitors• Implementation of complex webs of cause and effect• Dynamic alteration of the component execution

structure • Adaptation and evolution of the system

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ASW

MIW

SUW

IBGWN

011001111000010101000110101110101010010101011110100001111000111100101110101110

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Adaptive Mesh

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10100001111000111100101110101110001010001111100000111110jfdl;alskedj101

Fast and Flexible




Code Generation ExampleExample: Turret/Fire Control

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Slew Elevate

Fire When Slew and Elevate are Complete

Process Firing Commands (and Queuing Them)




Code Generation ExampleTurret: Fire Command

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void Turret::fire(){ P_VAR P_BEGIN(2)

// Wait until the turret movement is completed

WAIT_FOR(1, slewComplete, -1); WAIT_FOR(2, elevateComplete, -1);

// Fire the weapon, this would activate the real gun

Fire_M256(); RB_cout << "Flash, Boom, Bang, Echo" << endl; fireComplete = 1; P_END}




Demonstration Main Points

• Web and Real-time ESB Cooperation– RT ESB handles sustained ~ 1000 messages/sec with

modest hardware and complex systems

• Multiple, simultaneous inter-processor comms employed (shared mem, TCP/IP, RS-232 at 19,200 bps, UDP Broadcast)

• Great performance of complex applications (physics-based 3D visualization and motion prediction)

• Large number of diverse databases and data types employed




Demo Information Flow

ShadowUAV

SystemComponents

Battlespace Entity

Generator

C4ISR Network Interface (RS-232)

Sensor View 2

Sensor View 1

Web Message Service

C2PC Gateway

C2PC

RS-232 Interface

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C4ISR NetworkWeb-based/Non-real-time

Battlespace NetworkPredominantly real-time




UAV System

AirframeElectro-

MechanicalSystems

SuspensionWheels/Tires

Stability Augmentation

System

CommUplink andDownlink

Powerplant

Pilot Controller

ControlStation

Interface

BIASApplication

Interface

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Composability Example: EO/IR Camera

Physics-basedImagerModel

Electro-Mechanical

SystemsModeling

Physics-basedAtmospheric

Modeling

Open SceneGraph (OSG)

*3D Modeling*

OpenProducer

*3D Rendering*

OpenThreads

OSGnVidiaPlugin

CONDORProcessing

Engine

CONDORExternal

ApplicationInterface

IR Camera Simulation

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Most “intelligent” computer applications use rule-based expert systems (or some variant) as a means of storing expert knowledge, however these systems always have one or more severe limitations that make them unsuitable for future intelligent software development……..

BRITTLE……….ONCE PROGRAMMEDTHEY ARE EXTREMELY HARD TO

MODIFYCOMPLEX……….HARD (Expensive) TO MAINTAIN

SLOW……….CAN’T APPLY LARGE AMOUNTS OF KNOWLEDGE FOR REAL

TIME APPLICATIONS

RIGID……….CAN’T ADAPT OR SCALE WITH CHANGES IN SITUATION

The Problem

The Conceptual Graph approach was designed to overcome all of these common problems




KNOWLEDGE STORAGE USING CONCEPTUAL GRAPHS

New conceptual modeling approaches allow knowledge to be represented in an “Object-Relationship” graphical format - A

surprisingly high level of knowledge complexity can be represented using only three components:

Concepts ActorsRelationships

CAT STAT LOCSIT MAT

A Cat sits on a mat Une Chat assis sur une matte




Observer Actor

Conceptual Graphs

CONCEPTUAL REASONING ENGINE (CORE)

Stored Knowledge (Reusable)

INPUTS

OUTPUTS

OBSERVER ACTORS SENSE THE STATE OF THE KNOWLEDGE IN THE CONCEPTUAL GRAPHS AND TRANSLATE THAT INTO

THE SPECIFIC OUTPUT FORMAT NEEDED BY THE USER




Human Factors Architecture Development

Synthetic

Battlespace

Psychological Assessment

Behavior Moderator

Identification

Cognitive Architecture Modification

Human Commander

Human Commander

Psychological Characteristics

Behavior Moderators

Cognitive Architecture Moderators

Modifiable Cognitive

Architecture

Psychological Characteristics:

Traits:Performance UnderPressureConcentration StyleSelf-EsteemBehavioral ControlPhysical Orientation

/

CognitiveFocusDistractibilityFlexiblityInternal External Awareness

Human Factors Assessment Human Factors Modeling Model Validation

Behavior Moderators:

TraitsControlIntroversionExtraversionIntellectual ExpressivenessStatesAnxietyFrustrationObsessiveness

CognitiveAttention capacity / speedWM capacity / speedSkill

Cognitive Architecture Moderators:

StructuralArchitecture topology(Weights on intermodule links)Long-term memory

(Content & structure)

ProcessingModule parameters(Capacity and speed )

Inferencing speed & biases(Cue selection & delays)

orSynthetic

Environment

Psychological Assessment

Tools

(War)game




Internal Processes Analogy: The Heart Beat

• Atria pump blood to ventricles, which contract

• Nonstop contractions are driven by the heart's electrical system

Internal Process: Synchronous or Asynchronous – Intrinsic CapabilitiesInternal Process: Synchronous or Asynchronous – Intrinsic Capabilities




External Processes Analogy: Pacemaker

• External process monitors and interacts with an object (i.e., a pacemaker monitors the heart’s rhythm)

• The electric current makes the heart beat within a certain range

External Process: Synchronous or Asynchronous – Monitor and ControlExternal Process: Synchronous or Asynchronous – Monitor and Control




Internal Events Analogy: Heart Attack

• Internal occurrence without pre-established time scale

• Certain factors cause the occurrence. Blood flow is restricted, or the nerve system, which controls the heart, malfunctions

Internal Occurrence: Irregular Time Scale – Intrinsic CapabilitiesInternal Occurrence: Irregular Time Scale – Intrinsic Capabilities




External Events Analogy: Defibrillation

• External event changes a passive object’s state (i.e., a defibrillator is used for resuscitation)

• External electrical shock is applied to the heart

• Basic representation method

External Occurrence: Irregular Time Scale – Monitor and ControlExternal Occurrence: Irregular Time Scale – Monitor and Control




Conceptual Graphs

ConceptsConcepts ActorsActorsRelationshipsRelationships

CAT STAT LOCSIT MATCAT STAT LOCSIT MATMAT

A Cat sits on a matA Cat sits on a mat Une Chat assis surune matteUne Chat assis surune matte




Basic CG Formation Rules

Concept 1

Relationship 1

Relationship 1

Concept 2

Concepts or Other Actors

Actor 1

Actors or Relationships

Actors or Relationships

Concept 1

Concept

Concept or other Actors

Concept 1

Relationship 1

Actor 1

INVALID




CG ExecutionDividend: 9.0

Divisor: 4.0

Number: 144.0

Quotient: 2.0

Remainder: 1.0

SquareRoot: 12.0

Sum: 14.0

divide plus

sqrt

Dividend: 9

Divisor: 4

Number: 144

Quotient: 2

Remainder: 1

SquareRoot: 12

Sum: *s

divide plusplus

sqrt

IF ?r = 0IF ?r = 0

T

TT

T

TT

TT

T

F




CG Execution Example

runs

Is a

generating

for

Responsible for

of Producing

AMachine

AnInterpreter

Algorithm

AnInterpretation

An Agent

A Graph A Behavior




IBM Sun Oracle BEA RedHat II

Web-based SOA Stack X X X X X X

Heterogeneous Data Source Support X X X X X X

Semantic Web Integration X X X X X

Cloud Integration X X X X

Analog to Digital Interface Support X X

Legacy System Support X

Does Not Require Global Data Model X

Rapidly Adaptable to Change X

Two Day Learning Curve X

Automated Interoperability Assessment X

Sub-Microsecond Latency X

Hard Real-Time Performance X

Support 1M Active System Components X

Capability is Operational X X X X X X

IT Competitor Comparison




LM NG Boeing GD Raytheon II

Integration Middleware/Dev Env X X X X X X

Heterogeneous Data Source Support X X X X X X

Semantic Web Integration X

Cloud Integration X

Analog to Digital Interface Support X X X X X X

Legacy System Support X

Does Not Require Global Data Model X

Rapidly Adaptable to Change X

Two Day Learning Curve X

Automated Interoperability Assessment X

Sub-Microsecond Latency X

Hard Real-Time Performance X X X X X X

Support 1M Active System Components X

Capability is Operational X X X X X X

LSI Competitor Comparison

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