advanced decision architectures collaborative technology alliance dna of dare dynamic network...

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.

Advanced Decision Architectures Collaborative Technology Alliance

Challenge

• 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

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Two Case Studies

• 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

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Data Sources

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

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DNA Tools

DyNetML

Table 1:

Know-

ledge

Abdul Rahman Yasin

chemicals chemicals bomb, World Trade Center

Al Qaeda operative 26-Feb-93

Dying, Iraq palestinian

Achille Lauro cruise ship hijackin

Baghdad 1985

2000

Hisham AlHussein

school phone, bomb

M anila, Zamboanga

second secretary

February 13, 2003,October 3,2002

Hamsiraji Ali

phone Abu Sayyaf, AlQaeda

Philippine leader

1980s

brother-in-law

Abu Sayyaf,Iraqis

Iraqi

1991

Name of Individual

Meta-matrix Entity

Agent Resource Task-Event Organization

Location Role Attribute

Abu Abbas Hussein masterminding

Green Berets

terrorist

Abu Madja phone Abu Sayyaf, AlQaeda

Philippine leader

Abdurajak Janjalani

Jamal M ohammad Khalifa, Osama binLaden

Hamsiraji Ali

Saddam Hussein

$20,000 Basilan commander

Muwafak al-Ani

business card

bomb Philippines, M anila

terrorists, diplomat

Meta-Network

Unified Database(s)

Performance impact of removing top leader

64.5

65

65.5

66

66.5

67

67.5

68

68.5

69

1 18 35 52 69 86 103 120 137 154 171 188

Time

Per

form

ance

al-Qa'ida without leader

Hamas without leaderBuild Network -

Text Mining

Analyze –Statistics

SNA, DNA, Link

Analysis

Assess Change, What

if Analysis – Multi-agent

DNA

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Case Study 1

• 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

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Where is the Action?

Suspicious entities are fleeing Adelphi area over time course of scenario

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How are they Organized?

FOG (Fuzzy Group Clustering) shows suspicious entities organized into 5

groups w/shared members.

════════ Interstitial members are likely to contain coordinators & leaders.

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Who are the Key Players?

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.

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When is the Action?

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

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

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What Happened?

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

Yellow = LocationRed = Agent

Bold Red = Key Player

Advanced Decision Architectures Collaborative Technology Alliance

Who was Where, When?

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

*ORA Trails Viz════════

Time progresses down y-axis════════Geographic regions form

lanes on x-axis════════3 key players color-coded

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Reasonable COAs

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)

Purple = LocationRed = Agent

Advanced Decision Architectures Collaborative Technology Alliance

Case Study 2: Scenario

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.

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Case Study 2: Analysis

• 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.

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DNA Kick-start

• 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

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Which Agents were Involved?

*ORA Sphere of Influence

════════Tasking Agent 3 shares.

Is positioned in network differently from others & sends/receives unique messages.

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Psychological Validity of Newman Grouping

• 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

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Lessons Learned

• 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

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Next Steps

• 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)

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Future

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