# Presentation: 951196

## ATL & XMDRTechnologies Overview(Developed and Future Pursuits)

**Benjamin Ashpole**
_**[bashpole@atl.lmco.com](mailto:bashpole@atl.lmco.com)**_
**856-792-9744**

**Dr. Raj Kant**
_**[rkant@atl.lmco.com](mailto:rkant@atl.lmco.com)**_
**856-792-9730**

**http://www.atl.external.lmco.com/projects/ontology/**

## Introduction

**ATL Overview**
**Technology Topics**
**Ontology Alignment prototype**
**Software Agents technology**
**COgnitive Algorithm Composition Handler (COACH) concept**
**Dynamic and Static Application Analysis**
**Service-2-Service matchmaking**
**Explanation generation**
**Service Navigation & Execution**
**Security, Authentication**
**Evaluation**
**I3Con (2004)**
**EON (2004)**
**IC2, STS, MS2 (planned for 2005)**
**ATL and XMDR: Goals**

## Advanced Technology Laboratories Overview

**Jim Marsh, Director**
**(856) 792-9820**
**jmarsh@atl.lmco.com**

## Our mission ...
Solve world class information  technology problems
Provide a consistent stream of technology discriminators for military applications
Our formula ...
Advanced technology
Innovation in advanced computing and intelligent software
Exploitation and hardening of emerging technologies
Domain expertise
Path to a product
Integrated solutions with quantified payoff 
Proven technology transition
... leads to paradigm-changing payoffs

**Our mission ...**
**Solve world class information  technology problems**
**Provide a consistent stream of technology discriminators for military applications**
**Our formula ...**
**Advanced technology**
**Innovation in advanced computing and intelligent software**
**Exploitation and hardening of emerging technologies**
**Domain expertise**
**Path to a product**
**Integrated solutions with quantified payoff **
**Proven technology transition**
**... ****leads to paradigm-changing payoffs**

**Add FCS Picture?**

**Advanced Technology Laboratories**	... converting research into solutions

**Notes:**
Our focus and mission is to provide Lockheed Martin with discriminating information technologies for its next generation systems

From a business standpoint, we believe we have created a unique market position by providing a combnation of three things
The ability to take bleeding edge research, combine it, and apply it to solve real Military challenges
The ability to provide field-tested prototypes in a rapid, agile manner
The ability to transition these capabilities into real [Lockheed Martin] systems
We do this by bringing in a combination of high tech researchers and applied scientists who are very customer focused

## Advanced Technology Laboratories

**Advanced Technology Laboratories**

**Established in 1929**
**Key location: Cherry Hill, NJ**
**88K sq. ft. **
**Multiple labs (up to TS/SCI)**
**Core capability: Advanced Information Technology**
**Intelligent systems**
**Information architectures**
**Embedded systems**
**Wireless communications**
**Complex system simulation/analysis**

*Lockheed Martin*
*26%*

*DARPA*
*37%*

*Other 4%*

*2003 Customers*

*Full Time Employees = 148 as of 10/4/04*
*Non-ATL/Interns/Visiting Researchers = 14*
*Total = 162*

*Technologists - 78%*

*Management*
*7%*

*Other*
*15%*

*Gov. Labs*
*25%*

*• **PhD 17%*
*• **MS 49%*
*• **BS 34%*

*Classified 8%*

## Emerging Challenges Require  Compelling Technology

**Systems**
**Advanced Networking**
**Wireless Communications**
**Adv Signal Proc & Embedded Proc.**
**Complex Systems**

**Intelligent Systems**
**Network Centric Operations **
**Autonomy and Collaboration**
**Situation Understanding**
**Decision Support**

**Adaptive Information Systems**
**Dynamic Info. Integration **
**Information Extraction/Exploitation**
**Cognitive Computing**
**Network Mission Assurance**

**National Missile**
**Defense**
**Time Critical Strike**

**Homeland Security**
**Anti-Terrorism**
**IW**
**NBC**

**Joint/Coalition **
**Operations**
**Rapid Response **
**Info Superiority**

**Force Multipliers**
**Collaborative Auton. Vehicles**
**Human Augment.**

**Emerging Challenges Require  Compelling Technology**

- Business Areas

## Current DARPA Programs andTransition Targets

**Current DARPA Programs and****Transition Targets**

**Technology Focus**
***Human Machine Interaction***
***Situation Understanding***
***Plan Understanding & Monitoring***
***Autonomy & Teaming***
***Information Protection***
***Agent-Based Systems***
***Composable Simulation***
***Network Centric Enablers***

**IPTO**** **
**Aug Cog**
**ASSIST**
**COORDI-NATORs**
**RAP Teams**
**CRABS**
** **
**FAST C2AP**
**FM-UAO **
**PCA, ACIP**
**NA3TIVE **
**SAPIENT**

**IXO**
** ****DTT**
**JAGUAR**
**HURT**
**PCES**
**ARMS **

**ATO**
** **
**FTN**
**DTN**
**XG-Comms**
**Connection-less Nets**
**MNM**

**TTO**
**UCAR**
**UCAR**
**UCAR**
**UCAR**
**UCAR**
**MDC2**

**Gov. Labs**
**ONR, NWDC,**
**Marines**
**Army AATD, **
**CECOM, ARL**
**ONR, AFRL,**
** ****AATD**
** **
**Classified**
**NWDC, JFCOM, **
**JL ACTD**
**JTL ACTD**** **
**Various **
**(AFRL, NWDC, **
**CECOM, etc.)**

**R&D Emphasis**

**Transition, System Emphasis**

**Ongoing**

**Bid**

**LM BU**
**SI Owego (UCAR), MS2 (HAIL)**
**SI Owego (UCAR, HSKT, AMUST-D)**
**	****	**
**SI Owego (UCAR)**
**ADP Ft. Worth (AO FNC, Autonomy FNC )**
**	**
** **
**MS2 (DD(X), DW, LCS  Assured RT)**
**SI Owego (UCAR)**

**Notes:**
AugCog - Augmented Cognition - Dylan Schmorrow - Research on techniques to augment working memory of operators
Communications Symphony - natural language human-to-computer interface for field operations - moved from IAO
COORDINATORS - Achieve complex planning decisions across distribute units in bounded time
RAP Teams - Robots, Agents and People Teams - Dylan Schmorrow - Adapting human teaming methodology to mixtures of robots, agents and people
CRABS - Larry Jackel - Composable Reactive Agent BehaviorS (CRABS) - robotic control that combines some of the benefits of reactive and deliberative control to enable autonomous survivability in the face of active threats. 
CoABS - Control of Agent Based Systems - Dylan Schmorrow - 
FM-UAO - Force Multiplier for Urban Area Operations - Creates a model representing all influences on human behavior in pre- and post-conflict situations (social/cultural, infrastructure, governance, environment,terrain etc.)
PCA - Polymorphous Computing Architecture - Bob Graybill - Explore V&V requirements and technology for application of Polymorphous Computing Architectures (PCA) in avionics 
ACIP - Architectures for Cognitive Information Processing - BoB Graybill - A robust concept description of an integrated architecture, including hardware and software, that will efficiently support cognitive information processing as well as a wide variety of other run-time components
FTN - Fault Tolerant Networks (Was Doug Maughn-no current PM)
DTN - Disruptive Tolerant Networks - Preston Marshall - Fuzzy scheduling and bundle delivery, “Policy Based” resource planning, Late binding of delivery addresses in networks without assured connectivity
DACAM - Defense against Cyber Attacks on MANETS - Operating MANET networks while under cyber attack
XG-Comms - neXt Generation Communications - Preston Marshall - Real-time reallocation of spectrum to meet application needs and maximize spectrum use
Control Plane - Tim Gibson - Develop network protocols that learn or infer various infrastructure characteristics (e.g., maximum transmission unit size, link speed, packet loss rate, delay, available routes, etc.), to make transmissions more efficient
CN - Connectionless Networks, DARPA ATO, Preston Marshall PM; status - proposals submitted, waiting for announcements;  we bid as subs on proposals by Lucent and GE GRC.
MNM - Mobile Network MIMO (Multiple In Multiple Out), DARPA ATO, Jim Freebersyer - Sub to Lucent - using multiple transmission reflections off terrain combined with directional antennas to create additional comm channels.
Jaguar - Joint Air/Ground operations: Unified Adaptive Replanning - Bob Tenney - Semi- automating the ATO process and decreasing the required staff by factor of 10 or more.
Deep Green - Chuck Taylor - Next generation fusion system
HURT - Heterogeneous Urban RSTA (Reconnaissance, Surveillance, and Target Acquisition) Team - John Bay
PCES - Program Composition for Embedded Systems - Joe Cross - Develop suites of reusable software code aspects that implement cross-cutting system or middleware properties and that are in a form suitable for composition 
ARMS - Adaptive Reflective Middleware System - Joe Cross - Develop and validate the new generation of distributed real-time and embedded (DRE) middleware technologies to support DoD shipboard combat systems
UCAR - Unmanned Combat Armed Rotocraft - Don Woodbury - Develop key technologies that enable the next generation of autonomous & collaborative operations, enhanced survivability at altitudes above nap-of-the-earth,low-altitude autonomous flight, and standoff target identification.
MDC2

## Technology Topics

**ATL R&D Interests**

**Technology Topics**

## Technology Topics

**Technology Topics**

## ONtology TRAnslation PROtocol (Ontrapro) – Ontology Mapping & Alignment

**ONtology TRAnslation PROtocol (Ontrapro) – Ontology Mapping & Alignment**

**XMDR Use Cases: Research challenges**
**Semantic Normalization, Disambiguation, and Harmonization**
**Multiple entries within the same taxonomy**
**Similar but significant differences in the entries of two ontologies**
**Mapping and Interrelationships**
**Structure alignment of two different ontologies**
** ****ATL’s ONTRAPRO prototype addresses these challenges**

**Data Description****(Schema or Ontology)**

**Data**

**Data**

**Ontrapro**

## Example: Wine OntologiesTerm Dissimilarities(largely similar but not exactly same)

**Technology Topics**

## Technology Topics

**Technology Topics**

## Example: Wine Ontologiesand then....multiple graphical structure mappings

**Technology Topics**

## Technology Topics

**Technology Topics**

## Ontrapro – ongoing research activities

**Ontrapro – ongoing research activities**

## Coordination Overhead Comparison

**Coordination Overhead Comparison**

***Domain Standard***

***Ontrapro***

***O(n******2)***

***O(n)***

***O(1)***

***(no prior)***

- Manual
- Specialized Benefits
- Brittle-to-change
- No domain notion

- Manual
- Tradeoff for Generic
- Change-w/-Standard
- Single domain scope

**Automatic**
**Specialized Benefits**
**Change-at-Will**
**Multi-domain ready**

***Protocol***

***XML***

***OWL***

_***Effort, considerations, and time to wait***_* -- the number of interfaces developed by some party other than the service creator to learn before one may connect each service to each other, by agents, SOAP, etc.--** *_***are least for ONTRAPRO automatic alignment approach.***_

## Technology Topics

**Technology Topics**

## Software Agents – EMAA

**ATL has an agent architecture used on dozens of DARPA and DoD Service Lab contracts: The Extensible Mobile Agent Architecture (EMAA) is a suite of mature software modules.**
**XMDR Use Cases:**
**Support for Development of a “Universal” Grid**
**Unspecific (generic) domain**
**Resources discovered, navigated, composed together via some intermediating semantic metamodel.**
**Data Aggregation**
**Information retrieval by linking together resources into aggregate data reports.**
**Collating resources already registered within the XMDR registry.**
_**ONTRAPRO+Agents approach compliments the XMDR’s intermediating semantic metamodel.  Our approach uses Agents as work-flow mechanism.**_

## Software Agents – EMAA (cont.)

**ATL’s EMAA agents:**
**Comprise of a series of net-centric application resources described in OWL-S as web services**
**Are composed of building blocks, “tasks” that interconnect multiple web-service resources**
**Composition has been done dynamically through metadata planning, MPAC (Meta Planning for Agent Composition).**
**EMAA agents usually extract information reports by aggregating outputs of some services and feeding these reports as inputs to others**
**EMAA agents can also “enact” upon data by executing a pre-built processing instruction**
**ATL builds “Semantic Web Agents”, described in terms found from a well-connected ontology**
**Additional agent research at ATL also available for use: agent learning, adaptivity, and collaboration**

## Dynamic Agent Composition (example: charting Ship route in a channel)

**Y-axis has available sensors as web-services****X-axis has route-planners that use available sensor inputs**

## Dynamic Agent Composition(example: charting Ship route in a channel)

**Meta Planning of Agent Composition enables dynamic route planning**

**Dynamic Agent Composition****(example: charting Ship route in a channel)**

## Technology Topics

**Technology Topics**

## Dynamic and Static Application Analysis

**XMDR Use Case: Support a Data Grid**
**Ontrapro reduces the need for standards for interoperability**
**ATL has research interests in Dynamic and Static analysis applications to extract application’s inputs, outputs, and design intent**
**API & Static Analysis:**
**Examine software source-code documentation.**
**Design documents in UML**
**Whitepapers, conference papers, journal entries through NLP**
**Dynamic Code Analysis**
**At run-time analysis of object creation, method invocation**

## Technology Topics

**Technology Topics**

## Service 2 Service Matchmaking

**XMDR Use Case: Discovery, Location and Retrieval**
**Retrieve part or all of a terminology/concept structure**
**Retrieval based on related items: “data element, property, concept, class, domain, context, classification scheme, ontology”**
**Retrieve identity of registrar responsible for it**
**Ontrapro creates a comparison of ontologies as a result of attempting to align them**
**This allows us to find similar services *****semantically***
**We have additional algorithms to match service descriptions**

## Technology Topics

**Technology Topics**

## Explanation Generation

**XMDR Use Case: Help Support**
**“****A client application pulls metadata from the MDR in order to provide online help for an application end user.”**
**Provided from the registered application directly**
**OWL-S ******** Natural Language**
**Descriptions of a semantic web service converted to a human-understandable paragraph on what it does**
**Description of process**
**What we intend to have happen**
**What actually happened**

**Automatic and dynamic:**
** ****extraction of web-service spec/intent;**
** ****comparison with actual result;**
** ****generation of explanation;**
** ****NLP output**

## Technology Topics

**Technology Topics**

## Service Navigation and Execution Tools

**XMDR Use Case: Navigation**
**Applications uses MDR to support navigation of registered data elements and concepts between data elements**
**Execution Tools**
**Users could navigate components found in an MDR registry**
**Users could directly execute components if desired, from within the COACH framework**
**Parameter study and optimization tools built in**

***COACH***

## COgnitive Algorithm Composition Handler (COACH) concept

**XMDR Use Case: Navigation**
**Applications use MDR to support navigation of registered data elements and concepts between data elements**
**ATL is developing the COACH framework concept**
**Users could navigate components found in an MDR registry**
**Users could directly execute components if desired, from within the COACH framework**
**Parameter study and optimization tools built in**

***COACH***

## Information Interpretation and Integration Conference (I3CON)

**Information Interpretation and Integration Conference (I****3****CON)**

**Experiment Participants**
**Jerome Pierson (INRIA)**
**John Li (Teknowledge)**
**Lewis Hart (AT&T)**
**Marc Ehrig (University of Karlsruhe) **
**Todd Hughes (LM ATL)**
**Guest Speakers**
**Ben Ashpole (LM ATL)**
**Bill Andersen (Ontology Works)**
**Mike Pool (Information Extraction and Transport)**
**Yun Peng (University of Maryland Baltimore County)**
**Mike Gruningner (University of Maryland)**

## Information Interpretation and Integration Conference (I3CON)

**Information Interpretation and Integration Conference (I****3****CON)**

**August 24-26, 2004 in Gaithersburg, MD**
**ATL organized**
**Published paper**
**Positive Review in AFRL/IF Directorate Monthly Web Newsletter**

**Experiment Participants**
**Jerome Pierson (INRIA)**
**John Li (Teknowledge)**
**Lewis Hart (AT&T)**
**Marc Ehrig (University of Karlsruhe) **
**Todd Hughes (LM ATL)**
**Guest Speakers**
**Ben Ashpole (LM ATL)**
**Bill Andersen (Ontology Works)**
**Mike Pool (Information Extraction and Transport)**
**Yun Peng (University of Maryland Baltimore County)**
**Mike Gruningner (University of Maryland)**

## I3CON: Experiment Results (1)

## I3CON: Experiment Results (2)

## I3CON: Experiment Results (3)

## Evaluation of Ontology Tools Workshop

- Participants:
**Customers:**
*Dan Adams (NGA)*
*Sam Chance (NRL)*
*Kevin Keck (BNL)*
**U.S:**
*Mark Mayberry (Mitre)*
*Chris Priest (HP)*
**International:**
*Willa Wei (MDA)*
*Toru Ishida (KU)*
*Shigoeki Hirai (AIST)*
*Marc Ehrig (KU)*
*Jerome Euzenat (INRIA)*
*Alex Smirnov (RAS)*
*Marco Neuman (DIT)*
*Ian Horrocks (UM)*
*Jeremy Carrol (HP)*

***Contest results:***

## ATL and XMDR: Goals

**ATL has prototypes and concepts that can help solve some of the key XMDR use cases (as shown in previous slides):**
**ONTRAPRO, COACH, EMAA**
**ATL can build Semantic Web Services out of each of these technologies and enable XMDR to use them as parts of its architecture and prototype.**
**Support initial generation of ontology translators between web-services and service model**
**Support initial generation of ontology content for the prototype**
**Support easy migration of web-services from one version of service model to the next revision.**
**ATL technology compatible to the XMDR framework:**
**Usage of OWL, OWL-S, RDF and RDFS**
**Usage of SWRL, UML, and RDQL.**
**Most of our software written in Java.**
**ATL is actively seeking solutions to the other XMDR use cases.**

## Soothing Images for Question Time

**Soothing Images for Question Time**

## Backup Vgs

## Ontrapro accomplishments to Date

**Ontrapro accomplishments to Date**

**New, integrated alignment algorithms**
**Syntax aligners**
**Lexical aligners**
**Structural Aligners**
**Preprocessors**
**Filters**
**Enhanced display**
**Experimentation and evaluation of alignment performance**

## Cognitive Algorithm Composition Handler (COACH)

**Cognitive Algorithm Composition Handler (COACH)**

**COACH (Cognitive Algorithm Composition Helper)**
**Stable architecture**
**Composable experiment management**
**Enhanced GUI**
**Increased linkage to meta data**
**Serialization support**
**New runners and optimizers**
**Fine grained control over search spaces**
**Cluster extension**
**Enables massive parameter studies, optimizations, and simulation evaluations**
**Enables experience sharing between learners**

## Ontrapro Technology Status

| Capability | 2003 | 2004 | 2005 | 2006 |
| --- | --- | --- | --- | --- |
| String Align | Implemented | Expanded |  |  |
| Synonym Align | Implemented |  |  |  |
| Graph Align | Implemented | Expanded |  |  |
| Phonetic Align |  | Implemented |  |  |
| Integrated Aligners | Implemented | Expanded |  |  |
| Optimized Aligner Parameterization |  | 50% Implemented | Planned |  |
| Optimized Aligner Algorithms |  | 10% Implemented | Planned |  |
| Ontology Merging |  |  | Planned |  |
| Identity Align |  | 20% Implemented | Planned | Planned |
| Ontology Versioning |  |  |  | Planned |
| Legacy DB Alignment |  |  |  | Planned |
| Rule Alignment |  |  |  | Planned |

## Slide 43