# Presentation: 685586

## NOAATECH 2006

**NOAATECH 2006**
**Challenges of Distributed Automation**
**Dr. Shashi Phoha****Director, Information Technology Laboratory**
**National Institute of Standards and Technology**_[sphoha@nist.gov](mailto:sphoha@nist.gov)__, 301-975-2900_
**1 November, 2005**

## Shape of Things to Come

**Space-time coordinated Automation in an interconnected world: anytime, anywhere**
- Person to Person
- Person to Machine
- Machine to Person
- Machine to Machine
**  **

**Notes:**
This model reflects research on retention rates as they relate to instructional
approaches. Another great strength of the IBL model is that it allows students to 
immediately apply what they have learned by working on the FTP
 throughout the unit.   

## AUTONOMOUS OCEAN SAMPLING MOBILE SENSOR NETWORK (SAMON)

**AUV Platform**

**Ground Truth Platform**

**Tactical Coordinator  Platform**

## 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

## SYSTEM EVOLUTION PERSPECTIVES

**Perspectives**
- 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

## BEHAVIOR ENGINEERING AND 

- BEHAVIOR CONTROL

**Theoretical Advances**

## BEHAVIOR BASED CONTROL OF NETWORKED MISSIONS

- Discrete Event Control

## CAPTURING CAUSATION IN 

**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**

## 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**

- 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

- 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

## 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.

## 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 Expression**

**Perception**

- Physical World

- Cyberspace

- Communication Hardware

- Computer Hardware

## 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.**

## Shashi Phoha, Director

** ****Mathematical and **
**Computational **
**Sciences**
- Ron Boisvert

-  **Advanced **
**Network **
**Technologies**
- David Su

-  **Computer Security**
- Joan Hash, Actg.

**Information Access**
- Marty Herman

** ****Software **
**Diagnostics and **
**Conformance Testing**
- Mark Skall

**Statistical **
**Engineering**
- Kamie Roberts, Actg.

**Office of the Associate Director for **
**Federal and Industrial Relations**
- Kamie Roberts

**Technical Council**
- Invitees: Rene Peralta, Steve Seidman
- Internal: Thomas Rhodes, Steven Quirolgico, Larry Reeker (Facilitator)

**Executive Office**
- Kendra Cole, Senior Management Advisor

**Office of the Assistant Director for Boulder**
- Brad Alpert, Actg.

**High Speed**
**Network **
**Technologies**
- Nada Golmie
**Wireless**
**Communication**
**Technologies**
- Nader Moayeri
**Internetworking **
**Technologies**
- Douglas Montgomery

**Security Technology**
- William Burr
**Systems & Network**
**Security**
- Tim Grance
**Security**
**Management &**
**Assistance**
- Ray Snouffer, Actg.
**Security Testing & **
**Metrics**
- Ray Snouffer

**Speech**
- John Garofolo
**Retrieval**
- Ellen Voorhees
**Image**
- Charles Wilson
**Visualization &**
**Usability**
- Sharon Laskowski
**Digital Media**
- Wo Chang

**Software Quality**
- John Barkley
**Standards &**
**Conformance Testing**
- Lynne Rosenthal
**Interoperability**
- Barbara Guttman

**Metrology Statistics &**
**Computation**
- Nien-Fan Zhang
**Statistical Modeling &**
**Analysis**
- James Filliben
**Boulder Statistics**
- Jack Wang

**Mathematical**
**Modeling**
- Jeff McFadden
**Mathematical**
**Software**
- Dan Lozier
**Optimization &**
**Computational **
**Geometry**
- Ron Boisvert, Actg
**Scientific**
**Applications &**
**Visualization**
- Judy Terrill