noaatech 2006 challenges of distributed automation dr. shashi phoha director, information technology...

NOAATECH 2006

Challenges of Distributed Automation

Dr. Shashi PhohaDirector, Information Technology Laboratory

National Institute of Standards and [email protected], 301-975-2900

1 November, 2005

mailto:[email protected]

2

X-Internet

Forrester Research 2001

Electronic Chips

30 Billion

1.5 Billion

Telephones

Automobiles

663 Million

Internet Users 407 M

PCs 93M Space-time coordinated Automation in an interconnected world: anytime, anywhere

Person to Person

Person to Machine

Machine to Person

Machine to Machine

Shape of Things to Come

3

AUTONOMOUS OCEAN SAMPLING MOBILE SENSOR NETWORK

(SAMON)

AUV Platform

Ground Truth Platform

Tactical Coordinator Platform

4

DISTRIBUTED AUTOMATION

Transformational Power of IT

• Embed computation in the physical world

• Space-time coordination

• Take on functions of human interest- the dull, dirty and dangerous tasks

• Perform these in human time frames

Driving Applications• Fixed-sensors surveillance

• Fixed and mobile sensors surveillance

• Open field environment effects

• Noisy urban settings

• Distributed data fusion

• Automated tracking

5

SYSTEM EVOLUTION PERSPECTIVESPerspectives

• Network of Interacting Computational Machines• Dynamic system of Smart Sensors/Actuators

Human-Engineered Complex Dynamic Systems• Model Building: Discovering Structure in Data

• Discovering Causal patterns in RT Data• ε-machine Construction• Minimum Description Length

• Behavior Engineering• Functional Specifications of System

Behavior• Communications, Computation and Control

Language: C3L• Behavior Control

• Model Checking• Characterization of Control Pathologies• Automated Controller Generation

Emergent Behavior Prediction and Control• Predict behavior Patterns in Observed Data-

Forward problem• Engineer Local Machine Interactions to Achieve

Desired Behavior• Backward Problem

6

BEHAVIOR ENGINEERING AND BEHAVIOR CONTROL

Theoretical Advances

C o n t r o l l a b l e e v e n t

U n c o n t r o l l a b l e e v e n t

I n i t i a l s t a t e




S p e c i f i c a t i o n S C o n t r o l S t r a t e g i e s

E n g a g e e n e m y p l a n e s … E n a b l e

D o n ’ t g e t d e s t r o y e d … . . . D i s a b l e

a

B o t h … … … … … … … . . . U n c o n t r o l l a b l e

F i g h t i n g

A t B a s e

D e s t r o y e d

P l a n e s e n t o n m i s s i o n

M i s s i o n c o m p l e t e d

P l a n e s h o t d o w n

a

b

c

a

b

c

A l p h a b e t u U c

G e n e r a t e d M a c h i n e L a n g u a g e L

= S L

B e h a v i o r S p e c i f i c a t i o n L a n g u a g e :D e f i n i t i o n : I f L S * i s a p l a n t l a n g u a g e , t h e n a s p e c i f i c a t i o n S i s a

d e s i r a b l e s u b l a n g u a g e o f S * .

B e h a v i o r C o n t r o l l a b i l i t y :D e f i n i t i o n : I f S i s a s p e c i f i c a t i o n a n d L i s a p l a n t m o d e l , t h e n K = S L i s a c o n t r o l l a b l e s u b l a n g u a g e o f L i f :

KsuLsuKsuKs u













S p e c i f i c a t i o n S C o n t r o l S t r a t e g i e s

E n g a g e e n e m y p l a n e s … E n a b l e

D o n ’ t g e t d e s t r o y e d … . . . D i s a b l e

a

B o t h … … … … … … … . . . U n c o n t r o l l a b l e

F i g h t i n g

A t B a s e

D e s t r o y e d

P l a n e s e n t o n m i s s i o n

M i s s i o n c o m p l e t e d

P l a n e s h o t d o w n

a

b

c

a

b

c

A l p h a b e t u U c

G e n e r a t e d M a c h i n e L a n g u a g e L

= S L

B e h a v i o r S p e c i f i c a t i o n L a n g u a g e :D e f i n i t i o n : I f L S * i s a p l a n t l a n g u a g e , t h e n a s p e c i f i c a t i o n S i s a

d e s i r a b l e s u b l a n g u a g e o f S * .

B e h a v i o r C o n t r o l l a b i l i t y :D e f i n i t i o n : I f S i s a s p e c i f i c a t i o n a n d L i s a p l a n t m o d e l , t h e n K = S L i s a c o n t r o l l a b l e s u b l a n g u a g e o f L i f :

KsuLsuKsuKs u

BEHAVIOR BASED CONTROL OF NETWORKED MISSIONS

Discrete Event Control

T

S 1S 2

1 2 3 4 5

N e t w o r k C o o r d i n a t o r

C l u s t e r S u p e r v i s o r s

i

I n c r e a s i n g

I n t e l l i g e n c e

R e p o r t s H e a l t hG r e a t e r

P r e c i s i o n

O r d e r s

G e n e r a t o r s

54321

21

12111 22212

I n t e r a c t i n g A u t o m a t a M o d e l i n g N o n l i n e a r N o d e B e h a v i o r– G e n e r a t e e v e n t a l p h a b e t Σ– F o r m u l a t e o p e n l o o p f o r m a l– I n t e r a c t i n g F S A r e p r e s e n t a t i o n o f

P l e x u s d y n a m i c sC o m m o n C o n t r o l L a n g u a g e

– E v e n t / A c t i o n s e q u e n c e s i n C C LC o n t r o l l e r D e s i g n

– F o r m a l S p e c i f i c a t i o n s – C o n t r o l l e d S u b l a n g u a g e– C o n t r o l l e r s a r e l i k e p r o g r a m s i n

C C L t h a t r e s t r i c t t h e p l a n t b e h a v i o r t o s p e c i f i c a t i o n s

C o n t r o l E x e c u t i o n– C o n t r o l l e r s t r a n s l a t e t o s t a n d a r d

p r o g r a m m i n g s t r u c t u r e s a n d p r o t o c o l s

B e h a v i o r E n g i n e e r i n g– F o r w a r d P r o b l e m– B a c k w a r d P r o b l e m

T

S 1S 2

1 2 3 4 5



i

I n c r e a s i n g



P r e c i s i o n

O r d e r s

G e n e r a t o r s

T

S 1S 2

1 2 3 4 5



i

I n c r e a s i n g



P r e c i s i o n

O r d e r s

G e n e r a t o r s

54321

21

12111 22212

54321

21

12111 22212

I n t e r a c t i n g A u t o m a t a M o d e l i n g N o n l i n e a r N o d e B e h a v i o r– G e n e r a t e e v e n t a l p h a b e t Σ– F o r m u l a t e o p e n l o o p f o r m a l– I n t e r a c t i n g F S A r e p r e s e n t a t i o n o f

P l e x u s d y n a m i c sC o m m o n C o n t r o l L a n g u a g e

– E v e n t / A c t i o n s e q u e n c e s i n C C LC o n t r o l l e r D e s i g n

– F o r m a l S p e c i f i c a t i o n s – C o n t r o l l e d S u b l a n g u a g e– C o n t r o l l e r s a r e l i k e p r o g r a m s i n

C C L t h a t r e s t r i c t t h e p l a n t b e h a v i o r t o s p e c i f i c a t i o n s

C o n t r o l E x e c u t i o n– C o n t r o l l e r s t r a n s l a t e t o s t a n d a r d

p r o g r a m m i n g s t r u c t u r e s a n d p r o t o c o l s

B e h a v i o r E n g i n e e r i n g– F o r w a r d P r o b l e m– B a c k w a r d P r o b l e m

8

Raw Time- series Data

Symbolics to Formal Language

Symbolization: -Machines Construction Formal Language Representations of Behaviors

• Induce specific behaviors in the sensor network

• Observe continuous/discrete signal as a time-series

• Symbolize using -machine and convert to Finite State Automata (FSA) representation

CAPTURING CAUSATION IN PHYSICAL SYSTEMS:

C3L BEHAVIORS

9

INFORMATION DRIVEN DISCOVERY

Discovering Structure in Sensor Data

• Є-machine Construction• Tolerates non-linearities• Multi-time scale• Captures the computational

mechanics• Algorithm for Defining

Causal States• Capture all the predictive power

in data• Discovering Causal

Patterns• At the symbolic level• Defining the right abstractions

• Capturing the Computational Dynamics of the System

A. Ray, “Symbolic Dynamic Analysis of Complex Systems for Anomaly Detection”, Signal Processing, 84 (2004) 115-1130

S. C. Chin, “Real-Time Anomaly Detection in Complex Dynamical Systems”, Ph.D. Thesis, PSU, 2004

Formulating a Language Measure

)(),(

)(,)(

212121

01

LLLLLLd

LXI

the measure of the language starting at state i

the measure of the language starting at the initial statethe cost transition matrixthe forcing vectorthe identity matrix

i0XI

)(),(

)(,)(

212121

01

LLLLLLd

LXI



i0XI



i0XI

A. Ray and S. Phoha, “A Language Measure for Discrete-event Automata,” Proc. of the International Federation of Automatic Control (IFAC) World Congress b’02, Barcelona, Spain, July 2002.

Information Knowledge Management

Analysis

Use

Data

Retrieval Exchange

DecisionDecision

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Structured Electronic Knowledge: Semantic Web

Knowledge Economy: Innovative

Applications

Automated Knowledge Generation: Computational Intelligence

Automated Knowledge Discovery

• Data Driven Drug Discovery from Clinical Data• Developing Knowledge Communities –

Networks of Knowledge• Distributed Learning and Innovation

- Anywhere- Just-in-time- Control-based

• Knowledge, Place, Business- Creating value by Customer-oriented

Innovation• Measuring Knowledge and its Economic Effects

2.56

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Procedures Text Diagrams/Images

DesignData

Wrappers

Local GlobalSchema

Wrappers

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Wrappers

Local GlobalSchema

SummarySchema

SummarySchema

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Multimedia Assembly

2.56

0.99

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Retrieval

Links

QueryExecution-Search

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Retrieval

Links

QueryExecution-Search

Layers System output0 1 n

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Layers System output0 1 n

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11

Computational Model Validation

Blind Learning Systems: Intuitive Learning

• Not necessarily learning the causal structure that explains behavior

• Why electronic perception is computationally messy?

– Simulate the wrong abstractions

– Hidden causal patterns may be non-intuitive

• Why thinking machines failed?

Having Modeled the Causal Structure:

• Is the behavior of the system robust under the systemic inaccuracies in measured data?

Physical Model Validation Methods

MV&V – A Measurement Science for V&V of Computational Models of Physical Systems

NIST’s Five Step Metrology-based V&V

J. Fong, J. Filliben, R. Fields, R. deWit, B. Bernstein, “A Reference Benchmark Approach to V&V of Computer Models of High-consequence Engineering Systems”, 2004.

12

IT Hard Problem Humans can discuss differences and sort them out,

why can’t computers?

Preliminaries• Information is an abstract entity, and has

no universal physical constants associated to it

• The von Neuman architecture has inherent computational limitations:

– Matching parentheses problem

• Computation model segregates cyberspace from physical space requiring human cognition of an application and its computational language expression in cyberspace

Semantic Web: Universal Abstractions for Information Modeling

• Discover a unit of information• Formulate rules and relationships how units

coalesce into words• Formulate information grammars

– Rules of coalescing words to capture semantic hierarchies

• Express processes and control in this ontology• Information Metrology for computational

languages-expressiveness, complexity, etc.• OBJECTIVE information modeling• Enable computers to discuss and work out

problems!!

Digital Language Representation of Subjective Human

Cognition of a Problem

Software

Von Neuman Bridge: TCP/IP

Subjective

Expressio

n

Perception

Physical WorldCyberspace

Communication Hardware

Computer Hardware

13

NIST/ITL MISSION

Information Interoperability Standards

Computational Modeling of Physical Systems

Software Quality Assurance

Information Access

Critical Infrastructure Security

Support U.S. industry, government, and academia through measures, standards, and technology to enable new computational methods of scientific measurement, assure IT innovations for maintaining global leadership,

insert advanced IT in complex societal processes.

14

Shashi Phoha, Director

Mathematical and Computational

SciencesRon Boisvert

Advanced Network

TechnologiesDavid Su

Computer SecurityJoan Hash, Actg.

Information AccessMarty Herman

Software Diagnostics and

Conformance TestingMark Skall

Statistical Engineering

Kamie Roberts, Actg.

Office of the Associate Director for Federal and Industrial Relations

Kamie Roberts

Technical CouncilInvitees: Rene Peralta, Steve Seidman

Internal: Thomas Rhodes, Steven Quirolgico, Larry Reeker (Facilitator)

Executive OfficeKendra Cole, Senior Management Advisor

Office of the Assistant Director for BoulderBrad Alpert, Actg.

High SpeedNetwork

TechnologiesNada Golmie

WirelessCommunicationTechnologiesNader Moayeri

Internetworking Technologies

Douglas Montgomery

Security TechnologyWilliam Burr

Systems & NetworkSecurity

Tim Grance

SecurityManagement &

AssistanceRay Snouffer, Actg.

Security Testing & Metrics

Ray Snouffer

SpeechJohn Garofolo

RetrievalEllen Voorhees

ImageCharles Wilson

Visualization &Usability

Sharon Laskowski

Digital MediaWo Chang

Software QualityJohn Barkley

Standards &Conformance Testing

Lynne Rosenthal

InteroperabilityBarbara Guttman

Metrology Statistics &Computation

Nien-Fan Zhang

Statistical Modeling &Analysis

James Filliben

Boulder StatisticsJack Wang

MathematicalModeling

Jeff McFadden

MathematicalSoftwareDan Lozier

Optimization &Computational

GeometryRon Boisvert, Actg

ScientificApplications &Visualization

Judy Terrill

noaatech 2006 challenges of distributed automation dr. shashi phoha director, information technology...

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