Transcript J. Forester

Fusion Based Knowledge for the
Objective Force: A Science and
Technology Objective
Presented August 20, 2003
By Joan E. Forester
Workshop on Satellite Data Applications and
Information Extraction
Army Science Board* Estimates of
Technology Readiness for Select Fields
Technology Readiness Levels
Enabling Technologies
Aided ATR
Smart Portals to push pull
Mobile Wireless (pagers, PDA)
Malicious Mobile Code
Visualization - Presentation
Data Extraction
Virtual environment
Automatic routers, priorities
2004
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Data fusion, information fusion
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3
Secure Intelligent Agents
Encryption and authentication
Exploitation Algorithms and assist
RTIC
Future Internet
Individual Soldier Tech.
Collaboration Technologies
Sync Distributed Secure Data base
Secure Access Technology Biometrics
Translingual language transcription
Soldier Education
Associates
Next Generation Internet
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*ASB Study - Knowledge Management and Information
Assurance, dated 09/01
2008
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Commercial
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Joint Directors of Laboratories (JDL)
Fusion Levels
Level 0: Sensor-level target identification
- Processing raw data near the sensor
Level 1: Where is the enemy? (Multi-sensor correlation)
- Multi INT Correlation for highly detailed Enemy Situation
----------------------------------------------------------------------------
Level 2: What is the enemy doing?
- Aggregation for COP
- Interpreting activities in context
- Develop hypotheses about current ECOA
Level 3: What are the enemy’s goals?
- Future ECOA’s
- Predict Intent and Strategy
Level 4: How should we respond?
−
How do we redirect the ISR system to get better SU?
DARPA Programs Related to
Levels 2 & 3 Fusion
Where
What When
Who
Level 2:
Situation Refinement
Level 1:
Object Refinement
Why
How
Level 3: Global
Threat Refinement
How well
Level 4: Performance
Refinement
DATA FUSION PROCESSING
ENABLING TECHNOLOGIES
physical objects
individual
organizations
events
Evidence
Extraction
& Link
Detection
specific
aggregated
environment & enemy tactics
local
Dynamic
Data
Exchange
global
enemy doctrine objectives & capability
CoABS
DAML
RKF
CPOF
local
global
friendly vulnerabilities & mission
Dynamic
Tactical
Targeting
Battle
Adv.
Assessment &
ISR
Data
Mgmt
Dissemination
Ref: DARPA IXO(SUO-SAA)
Information Fusion Workshop, final briefing, 28 Feb 2002
options needs
effectiveness
battle
theatre
resource management
local
global
Why We Need Fusion
Information volume exceeds war-fighter capabilities to develop situational
understanding required for planning and acting within the adversary’s decision cycle
Echelon
# Msg’s per hour*
# full time Analysts,
w/ workstations
Latency for
Level III Fusion
15
1 Hr
Legacy Division
400-600
Future UA Bde
17,000**
0-6 (TBD)
NRT (req)
Future UA Bn
4,000**
Zero
NRT (req)
Future UA Company
1,200**
Zero
NRT (req)
* Current and estimated bottom-up sensor feeds; Top-down feed is much larger
** (Date) Sensor briefing from CG, USAIC&FH to Dir, UAMBL / MAPEX indicates an order of magnitude increase
Reports Without Fusion
Bde COP
UE
Bn COP
Plus…Information from
echelons above UA
170K+ Reports/Hour
Report count based on
DCGS-A MAPEX
results using Caspian
Sea Scenario
Reports generated from FCS EO/IR and COMINT Sensors only.
Add MASINT sensors and reporting at UA goes to @ 600K/hour.
Co COP
56K+ Reports/Hour
18K+ Reports/Hour
PLT COP
Mr. Hayward’s Brief, Force Operating
Capability (FOC) S&T Assessment Review
6K+
Reports/Hour
FCS C4ISR Software Brick
“Fusion” is a component of the C2
Application Software
Warfighter Machine Interface (WMI)
Administration
Applications
DoD Enterprise
Applications
Information
Warfare
Comm Systems
Airspace Mgt
C2
Weapon Sys
Mission Applications
Vehicle Applications
SOS/Domain Application Programming Services (API’s. Applets/Servlets, …)
SOS Knowledge Management Services
Info Access
& Control
Services
Interoperability
Services
Security
Services
Network Centric
Services
Distributed
Framework
System,Fault Mgt,
Health Monitoring
Information
Discovery
Services
Information
Dissemination
Services
Tactical
Configuration
Services
User Profile
Services
SOS Framework Services
System
Agent Framework
Services
Services
Inference Engine
Services
Web
Services
Security
Services
Routing
Services
Distribution Middleware Services
Logical
Storage
Database
& Retrieval
Operating System Abstraction Services
Virtual
Memory
Run-Time
Process
Threads
Sockets
Select IO
Comp
Dynamic
Linking
Memory
Mapping
Operating System
Virtual Memory
Communications
COTS
NDI
Common
Support
Services
Process/Threads
Network Foundation – (e.g, LAN, Hardware Device Drivers )
Shull FCS LSI Concept Brief at MAPEX
COTS
NDI
The Objective Force Sensor Grid
Interdependent, Multi-Echelon, Cross-BOS,
Net-Centric
SPACE SYSTEMS
U2R
Direct Linkage(s) to
CDR, Staff & Shooters
Joint ISR to
FCS Via DCGS-A
GLOBAL HAWK
RJ
MC2A
JSTARS
ACS
ACS
ALLIED/
COALITION
TUAV
PREDATOR
UA (I)
UA (I)
UA (I)
UA (I)
PROPHET
Theater
Presented by
Col Ron Nelson
11 Dec 02
DCGS-A
UE
Right Information…Right Time…to the Point of Decision
2R
FBKOF:
Overcoming Information Overload
BARRIERS
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•
•
•
•
Limited computational models
Knowledge is METT-TC dependent
COTS knowledge acquisition technology
too slow
Information sources poorly integrated
Knowledge discovery tool limited
APPROACH
•
•
•
•
•
Cognitive engineering and user-centered
design
Apply Blackboard architecture, diverse
knowledge representation and
inferencing, approximation techniques,
and “to each his own” cooperative
human-machine problem solving
Exploit DARPA rapid knowledge
formation technologies to develop
knowledge-intensive reasoning for
interpretation
Leverage Semantic Web techniques for
source integration.
Integrate and tailor COTS tools for
directed knowledge discovery
DELIVERABLES
•
•
•
•
SW for knowledge generation and
explanation to answer PIR’s in a timely
manner
Ontology based information agents for
objective force systems
User-directed knowledge discovery tools
Modeling and simulation tools
Schedule
FY03 FY04
Tasks
FY05
FY06
FY07
• Baseline / Assess Knowledge
tools and Fusion Algorithms
2
3
4
• Knowledge Acquisition
2
3
4
3
4
• Mining-Component Development
• Knowledge Infrastructure Development
3
4
5
• Modeling and Simulation Support
• C4I experiments and evaluations
• Transitions Decision Points
TOTAL $24.3
CECOM
ARL
1.7
2.1
2.2
2.1
3.0
2.1
4.0
2.1
4.0
1.0
Notional Blackboard Architecture
for Fusion Subsystem
Levels of
Analysis
Answers to PIRs
COAs and COA
Fragments
Relations between
objects (command
hierarchy, behavioral)
Events &Activities
Objects
(equipment and
platform-level
entities)
Knowledge
Sources
Blackboard
Plans KS
• History
:
• Doctrine
•
Terrain & Weather
•
Activities KS
• Force Structure
:
• Commo Patterns
• Tactics
• Terrain & Weather
Sensor-Data Fusion KS
:
• Platform & Equipment
Classification and
Movement Attributes
• Terrain & Weather
CONTROL
Providing a Knowledge Environment
(Agents and Ontologies)
Interfac
e
Data- DataDatabase
base
base
OLAP
GOALS
•
•
•
•
•
•
•
•
Minimize burden on user
–
Automate well-structured problems
–
Support ill-structured problems
Interface tuned to the task and to the user
Task centered, not tool centered
Support information push and pull
Support collaboration
Accommodate multi-modal data types
Visualization tools to support understanding
Smarter integration of sources
DBMS
Knowledge
Base
Fusion
–
Limit the number of required retrievals (bandwidth)
–
–
Minimize exploration after retrieval (time constrained)
Automate and personalize the process
Interfac
e
Web
Search
Engine
Why Agents?
What are they? (ATL)
•
•
•
•
The concept of software agents represents a new way of applying artificial
intelligence techniques such as machine reasoning and learning.
Software agents are computer programs designed to operate in a manner analogous
to human agents. Human agents, such as real-estate agents, carry out tasks on
your behalf using expertise you may not have. Software agents carry out
information processing functions in the same manner.
Agents can be thought of, in software engineering terms, as a step beyond the
objects of object-oriented programming. Whereas objects are passive entities that
must be invoked to execute, agents use AI mechanisms such as machine reasoning
to actively operate as autonomous entities.
Research has shown greatest utility in multi-agent applications is information mgmt.
How do they help?
•
•
•
•
•
Huge problem broken into small components
Much can be handled in parallel rather than serially
Reflect changes in priorities without coding changes
Technology is coming of age
Many web applications [6, 9]: mediator, personal assistant
Active, persistent sw
components that
perceive, reason, act
and communicate
-- Huhns
Agent Functionality
•
•
•
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•
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•
•
•
Filter (ATL)
Monitors (ATL)
Alert (ATL)
Retrieve – pull (ATL)
Disseminate – push (ATL)
Adapt to the user priority (CTA, OH S U)
Adapt to the environmental changes (CTA, OH S U)
Mediate across legacy systems (UMD)
Intruder detection (HPC, UMINN)
Policy enforcement (CTA, U W Fl)
PROBLEMS
• Agents new, few success stories and limited developmental environments
• Present complex parallel processing paradigm
• Issues of teaming, security, mobility, efficiency
• Establishing optimum ontology size/approach
• Integrating ontologies across heterogeneous sources (single, multiple, hybrid)
Why Ontology-Based?
• Information heterogeneous (type, syntax, semantics)
• Heterogeneity of semantics results in conflicts (naming, scaling, confounding)
• Ontologies explicitly describe information sources
• Identify and share formal descriptions of domain-relevant concepts
• Identify classes of objects and organized them hierarchically
• Characterize classes by the properties they share
• Identify important relationships between classes
Brigade level
Mediator
Agent
DCGS-A Data Store
Single-INTs
Fusion
Prioritzer
Reasoner
Agent
Commo
Module
Agent
Ontology
COMINT
ELINT
MASINT
Imagery
Images/ Video/ Audio
MTI
HUMINT
Other Multimedia
Open Source
External COPs
(above/below/beside)
COP COP COP
COP
COP
MIDB
Blue
Asset
Mgmt
Terrain
Weather
Targets
CCIR/
IR/
OPLANs
Alert/
Search
Criteria
All Source Fusion
(ASFDB)
Units Pieces of Equipment
Facilities
Events
Individuals
Organizations
And their interrelationships
Providing User-Directed Knowledge
Discovery Tools
•
•
•
•
•
•
•
•
On Line Analytical Processing (OLAP) emerged in the early 90’s (Inmon, Codd)
Multi-dimensional data structure
Better (more flexibly) address decision process (forecasting, time-series
analysis, link analysis)
More natural & efficient storage and retrieval mechanism
Provides a mechanism for accommodating time and space
Flexible graphical interface
Commercial Product
Natural Transition to Data Mining
PROBLEMS
•
•
•
Representation of space and time
Complexity of user interface
Inefficiency of algorithms
Partners and Leveraged Programs
•
•
•
•
•
•
•
•
•
•
RDECOM(provisional) RDEC I2WD
Army G2 (Woodson/ ISR Working Group)
Huachuca (Schlabach – Cahill)
BAH (Brown - Army MI SME)
ADA CTA (U W Fl, UMD, SA Tech)
ARMY HPC Program (UMINN, Data mining)
ARL CENTERS OF EXCELLENCE (CAU, Data mining)
PENN State (Yen, Teaming Agents)
C2CUT and Warrior’s Edge
DARPA: Taylor (RKF, Staff Officer in a Box); Alex Kott
(AIM); Burke (DAML, CoABS Grid)
• ENDORSEMENTS: BCBL-H; BCBL-L; PM DCGS-A, PM
IE, PM FCS
Status
FY03 : (1) Conduct cognitive engineering with SME to identify users’ goals, tasks and info requirements
most germane to the Intel BOS in support of higher level fusion -- identify candidate tasks to focus on in
FY04. (2) Develop initial human-machine and software-level evaluation plan for fusion. Design and
conduct pilot experiment for fusion. (3) Develop a small prototype Knowledge Environment (KE) that
uses agent techniques to access the two highest priority data sources. This will establish a baseline system
on which to build in out years, demonstrate our initial concept of the use of ontologies by the KE agent
communities, and provide a mechanism for integrating CECOM’s fusion modules. (3) Conduct an internal
demonstration of the baseline system to support refinement of the HCI/KE concepts.
FY04 : (1) Integrate two more data sources into the baseline system to assess the extensibility of the infrastructure and
provide the CECOM fusion module access to a greater variety of data sources. (2) Develop and populate a prototype
(1)mining
User or
involvement
multi-dimensional data structure for user directed data
knowledge discovery (KD). This will allow us to
explore the use of user-in-the-loop fusion tools to supplement CECOM automated fusion techniques. (3) Conduct an
(2) Working with ATL’s EMAA to establish
internal joint CECOM/ARL demonstration to refine the HCI and KE concepts.
mediators and baseline architecture.
FY05 : (1) Modify the KE system architecture, based on the FY04 evaluation and integrate 5th data/information source.
Warrior’s Edge (WE) link may increase the
(2) Jointly demonstrate to DCGS-A and user communities the integration of CECOM’s fusion algorithms, the usersize review
and scope
this effort.
ATL
alsodevelopers
directed KD tools and 5 data sources. This provides a formal
for theof
targeted
transition
system
a portion
of CECOM’s
fusion effort.
(FCS/DCGS-A) of the refined approach at a point when working
all the required
components
are in place.
FY06 : (1) Finalize user-directed mining scripts and
architecture,
based on FY05
The goal will be to
(3)system
Internal
demo scheduled
forevaluation.
late September.
simplify access to the KD tools. (3) Develop information
agentsvisible
to support
I2WD
fusion
task.with
These
agents will be
More
demo
may
occur
WE.
directed toward increasing the efficiency and effectiveness of information push/pull. (2) Internally demonstrate
automated cross-source integration using the enhanced
environment
and work
withdemo
CECOM to evaluate and
(+) agent
CAU/UMINN
Date
Mining
enhance the system’s functionality.
FY07 : (1) Finalize system development, based on FY06 evaluation. (2) Jointly conduct the final system demonstration
and evaluation to support system transition to FCS LSI contractor, PM-CGS, and PM-IF.
Conclusion
• Goal: Facilitate quick war fighting decisions that fully leverage the
huge volumes of information that the UA will receive.
– RDECOM I2WD user-centered fusion system design (architecture,
inferencing techniques, algorithms, representations, and HCI)
– ARL knowledge management infrastructure
– ARL user-directed knowledge discovery tools
• Proposed relatively modest software readiness levels, due to
difficulty of the task, but driving to get a transition:
– PM DCGS-A demonstration in 05, with a transition decision point in 07
– PM IE demonstration in 05, transition decision point in 07
– Demonstration to FCS LSI 05, AMSAA transition decision point in 07
• Data mining resources far exceed initial expectations.
• First year of agents development will receive a boost from related
ARL programs (C2CUT, Warrior’s Edge)
• Strong support from user community