Download Presentation

Advanced Decision Architectures Collaborative Technology Alliance
DNA of DARE
Dynamic Network Analysis
Applied to Experiments from the
Decision Architectures Research Environment
Kathleen M. Carley, Michael K. Martin,
Carnegie Mellon University
John P. Hancock
ArtisTech, Inc.
Challenge
Advanced Decision Architectures Collaborative Technology Alliance
• Apply DNA to simulated battlefield data being
generated in the DARE to produce tactically
relevant insight
• Apply DNA techniques to complex problems
where good solutions were lacking
– Adversarial communications data
– Distributed embedded Intelligent Agent messaging
– Working toward real-time Monitoring and Prediction
• Support Army and DoD requirements in
Intelligence and Net Centric Warfare problems
Two Case Studies
Advanced Decision Architectures Collaborative Technology Alliance
• Adversarial reasoning
– intercepts of simulated communications
among humans
• Movement in the perimeter
– Automated control of Persistent Coordinated
Video Surveillance (PCVS) system
– simulated communications among software
control agents & robots
Data Sources
Advanced Decision Architectures Collaborative Technology Alliance
ADA CTA Decision Architecture
Research Environment (DARE)
• Persistent Coordinated Video
Surveillance (PCVS) experiments
• Experimentally exploring the impact
of automated reasoning on PCVS
AlgoLink Entity/Link Simulation
• User specifies organizations (types,
sizes), locations, duration,
frequency…
• Provides a “Ground Truth” file
• Designed to test intelligence tools
DNA Tools
Advanced Decision Architectures Collaborative Technology Alliance
Analyze –
Statistics
SNA, DNA,
Link
Analysis
Build
Network Text Mining
Meta-Network
Table 1:
Name of
Individual
Abdul
Rahman
Yasin
Abu Abbas
Meta-matrix Entity
Agent
Knowledge
chemicals
Hussein
Hisham Al
Hussein
school
Abu Madja
Hamsiraji
Ali
Muwafak
al-Ani
Task-Event O rganizati
on
chemicals
bomb,
Al Qaeda
World
Trade
Center
Dy ing,
Green
Berets
Achille
Lauro cruise
ship
hijackin
p hone,
bomb
p hone
Hamsiraji
Ali
Abdurajak
Janjalani
Resource
mastermindi
ng
p hone
Jamal
M ohammad
Khalifa,
Osama
bin
Laden
Saddam
Hussein
Abu
Say y af,
Qaeda
Abu
Say y af,
Qaeda
Location
Role
Attribute
op erative
26-Feb-93
terrorist
p alestinian
1985
Iraq
Baghdad
M anila,
Zamboanga
second
secretary
Philip p ine
leader
Philip p ine
leader
Basilan
commander
2000
February
13,
2003,
October
3,
2002
DyNetML
Al
Al
1980s
brother-inlaw
$20,000
business
card
bomb
Abu
Say y af,
Iraqis
Philip p ines, terrorists,
M anila
dip lomat
Iraqi
1991
Unified Database(s)
Assess
Change, What
if Analysis –
Multi-agent
DNA
Case Study 1
Advanced Decision Architectures Collaborative Technology Alliance
• AlgoLink output delivered as XML file
– Log of simulated comms intercepts
– Each record identified sender, receiver, comm
time, comm duration, operational relevance of
content, lat/lon of sender & receiver
• DNA strategy = overview + zoom
– Engaged subset of *ORA capabilities
– Geospatial visualization, key player ID, change
detection analysis, & correlation of standard
and geospatial visualization.
– Did not use DNA text or simulation capabilities
Where is the Action?
Advanced Decision Architectures Collaborative Technology Alliance
Suspicious entities are fleeing Adelphi area over time course of scenario
How are they Organized?
Advanced Decision Architectures Collaborative Technology Alliance
FOG (Fuzzy Group Clustering) shows
suspicious entities organized into 5
groups w/shared members.
════════
Interstitial members are likely to
contain coordinators & leaders.
Who are the Key Players?
Advanced Decision Architectures Collaborative Technology Alliance
Drilling down…
*ORA’s Key Entity Report shows
3 agents critical to operations.
════════
Narrow our focus from
set of interstitial members to small
group of leaders.
When is the Action?
Advanced Decision Architectures Collaborative Technology Alliance
A planning-execution phase-shift…
════════
Organizational behavior changed in Period 2;
radical difference by Period 3
Change Detection Analysis
════════
Operation most likely in Period 3…Hidden, distributed
structure coordinated into centrally controlled unit at
Period 3; Hiding again by Period 4
3 key players’ behavior changes at Period 3.
════════
Agent 286 engaged in extensive coordination
at Period 2. Reigns of control passed to Agent
652 at Period 4
What Happened?
Advanced Decision Architectures Collaborative Technology Alliance
Yellow = Location
Red = Agent
Bold Red = Key Player
Period 3: Operation
════════
Large cluster of suspicious entities in
Adelphi area (with 286)
════════
Cluster in apparent staging area (w/97)
════════
Cluster associated with the runner, 652
Who was Where, When?
Advanced Decision Architectures Collaborative Technology Alliance
*ORA Trails Viz
════════
Time progresses
down y-axis
════════
Geographic
regions form
lanes on x-axis
════════
3 key players
color-coded
Period 4: Initial Surveillance
════════
Key players never in same place at same time
════════
Agents 286 & 97 (cyan & yellow) move about in their one region
════════
Agent 652 (green) moving through many regions
Reasonable COAs
Advanced Decision Architectures Collaborative Technology Alliance
Purple = Location
Red = Agent
Period 6: End of Data Stream
════════
COA 1: Scour Adelphi for bomb, IED, etc. planted during ops in Period 3
════════
COA 2: Go after dispersed suspicious entities (286 may be an easy target, but
the location where 97 is hiding will yield more suspicious entities)
Case Study 2: Scenario
Advanced Decision Architectures Collaborative Technology Alliance
ARTEMIS-PCVS System
════════
Tasking Agents control robotic
surveillance assets in 1 of 4
quadrants to identify entities
moving within perimeter of Blue
Force compound.
════════
Scenario starts with short period
of quiescence, followed by inject
of many moving targets that
cross Tasking Agent AORs.
Case Study 2: Analysis
Advanced Decision Architectures Collaborative Technology Alliance
• Output from ARTEMIS-PCVS system
delivered as XML file
– Log of simulated communications among
software agents & robots
– Each record identified sender, receiver,
communication time, message type
• DNA strategy = converging operations
– More data but structurally redundant
– Goal = detect rare handoff event where
Tasking Agents share robotic assets.
DNA Kick-start
Advanced Decision Architectures Collaborative Technology Alliance
• ArtisTech manually analyzed data
– Meticulous message-trace analysis & event
identification
– Identified message-types that indicate handoff
– Identified number of handoffs
• CMU CASOS employed DNA techniques
in *ORA to replicate ArtisTech’s analysis
Which Agents were Involved?
Advanced Decision Architectures Collaborative Technology Alliance
*ORA Sphere of Influence
════════
Tasking Agent 3 shares.
Is positioned in network differently from
others & sends/receives unique messages.
Psychological Validity of
Newman Grouping
Advanced Decision Architectures Collaborative Technology Alliance
• Per ArtisTech, agents can be partitioned
– Foreground agents  substantive role
– Background agents  housekeepers
• Manual analysis took more than 4 hours
• *ORA Newman Grouping in seconds
– 28 of 29 foreground agents correctly classified
– 7 of 10 background agents correctly classified
• ArtisTech raters disagreed on status of 1 of the
mismatched agents
Lessons Learned
Advanced Decision Architectures Collaborative Technology Alliance
• Open collaboration between data
providers & network analysts creates
beneficial gap between expected &
observed multi-agent system behavior
• Dynamic Network Analytics foster
– Understanding of emergent & reactive
behavior in multi-agent simulations (V&V)
– Tactical insight and development of COAs
• DNA can be used to assess realism of
data generation simulators
Next Steps
Advanced Decision Architectures Collaborative Technology Alliance
• Geo-spatial anchoring capabilities
– Support shifting among perspectives provided by network,
trails, and map visualizations
– Support locating key entities in different representations
– Improve ability to correlate socio-network & geo-spatial viz
• i.e., automate generation of the annotated viz that shows where
key players are in social network & on map
– Create capability for correlating trails viz & geo-spatial viz to
give fine-grained spatio-temporal view of movement
• Tactical insight wizard
– Codify set of analysis & viz techniques deemed useful for
generating COAs for different tactical situations
– Reports would generate the entire DARE poster for example
– Reports would differ in context-specific ways
• e.g., depending on whether the data are intercepts of
communications of among humans (study 1) or software/robot
agents (study 2)
Future
Advanced Decision Architectures Collaborative Technology Alliance
Time Frame
• Low-hanging Fruit
• Intermediate Range
• Long Range
Product
• Automate pre-processing for
*ORA input
• Build & automate Tactical
Insight Report
• DNA Monitoring of the
battlefield
• Real-time DNA of evolving
battlefield
• DNA based prediction