Concept of EmploymentAnd 

Technology Transition

Dr. James LlinasCenter for Multisource Information FusionState University of New York at Buffalo

Buffalo, New York, USAllinas@buffalo.edu

1

• Basis for Effectiveness/Operational Value Assessment for most Information Fusion/AI technologies

• Framework for:– Architectural Design: fit of IF software to Operational Infrastructure—critical Interfaces, standards compliance, etc

• Main driver for defining the human role– Understanding and addressing Operational Reqmtsimputed onto IF design—eg nature of Actionable Intelligence, OpTempo (OOSM), Predictive Reqmts, HCI and Visualization, etc

– Technology Transition, to 6.3 type Army programs

Why address Concept of Employment?

COE Alternatives

Weak A Priori Deductive Knowledge(Second order Uncertainty)

COURSE OF ACTION DEVELOPMENT AND OPTIMIZATION*

Outcome/Effects Modeling Knowledge Base

Exploitation of A Priori Deductive Knowledge

Decision / Action‐Taking; COA Optimization

DARPA Compoex Pgm, Conflict Modeling, Planning and Outcomes. Experimentation Program 2007

COE AlternativesMURI Approach:  No Reliable Dynamic COIN Model;Dynamic Discovery / Learning‐Based Approach

Learning‐Based                                            SNA‐BasedUnsupervised Pattern Discovery                Frequent Subgraph DiscoverySupervised Concept Learning                      Wide Variety of Centrality MetricsStatistical Relational Learning                     Community DetectionQuery‐Response‐Based Learning                Random GraphsPIR State Discovery

Chung, W., et al, Identifying and Tracking Dynamic Processes in Social NetworksDas Sarma, A., et al, Dynamic Relationship and Event Discovery

Focusfor Inferencing 

and COE Research

Discovery/LearningCOE

Set of Target Graphs depicting Complex Relationships

Of Interest

Associated Hard‐Soft Incremental

Evidential Graph

PIR Indicator‐Graphs (A Priori & Runtime)

MOS 35D

VisualizationQuery‐Formulation Support

SNA  Tool

GM/Learning Tool

PIR, Pattern AssertionsBasis forLearning

Multi‐Paradigm Toolkit

~ Query

Graph‐matched Response sequence

Identifying Notional Usersfor Army PIR Analysis

IntelligenceSection

Brigade Combat Team

Commander

ISRSynchronization

Plan

Intelligence

LOEDesired Effects

MOE’s‐‐Measurable‐‐Tangible

Operations Lines of Effort

PIRRunning Estimate

Functional Area Officers‐‐Governance

‐‐Essential Services‐‐etc“The only mission of the Intelligence Section

is to answer the commander’s PIR “

PIR Development‐‐Nominate, iterateSIGINTHUMINT

IntelligenceSection

IMINTOPSINT

SIGSUMHUMSUM IMSUMOPSSUM

CollectionSection

ProductionSection

All‐sourceAnalysts

Tag to PIR

PIRSection

All‐sourceAnalysts

ExternalSources

PIRWorkingGroup

‐‐Intell; all INTs‐‐LOE‐‐Operations

PIR Gaps

ISR Synch Plan

Officer Title  MOS Code Skills All‐Source Intelligence Officer 

35D  All MI officers receive initial and advanced training as a 35D. Duties include directing, supervising, and coordinating the planning, collection, processing, production, and dissemination of all‐source intelligence (HUMINT, IMINT, MASINT, SIGINT, OSINT, and CI) at all echelons. They perform multidiscipline collection management, coordination of surveillance and reconnaissance activities, and provide advice on the use of intelligence resources at all echelons. They supervise and perform IPB using automated intelligence data processing systems and advise the commander and subordinate units on the enemy, weather, and terrain. 

    

Table 1: IPB vs. PBA IPB PBA

Intel-centric Commander-centric Product-centric Process-centric Structured, iterative process Dynamic Process Focuses on red as an independent actor Emphasizes red-blue-gray interaction and

interdependencies Describes adversary’s courses of action Emphasizes commander’s anticipation and

pre-emption of the adversary through decisive effects

Focuses on courses of action initiated after inception of conflict/crisis  Generally delimited by specific geographical boundaries (operational and/or tactical levels)

Enables the commander to shape the pre- post-conflict environment to his advantage; reduces uncertainty during conflict/crisis Provides continuity of awareness from the strategic to the tactical level of operation

Focuses on target identification / definition Focuses on commander’s decisions to produce decisive effects in the battlespace

Stovepiped processes and information-flow Horizontally, vertically integrated processes; ubiquitous information through the publish-subscribe-broker information architecture

TechnologyTransition

• Completed– Preliminary GM (Trust) algorithm to ARL (M. Thomas)– SYNCOIN Data Set – shared to:

• US‐UK Intl Technology Alliance (via Lance Kaplan, ARL)• BAESystems, UK (via Dave Nicholson, BAE)

• In‐Process– ARO Infrastructure Proposal (Transition to ARL)– Advanced All‐source Sensor Fusion (A2SF)‐I2WD, M. Patel– Possible Network Science CTA ~ Inquiries from Dr. Kott– Possible DRDC Canada– Possible US‐UK Intl Technology Alliance Pgm and OSD D2D ~ mtgs Sept 29

ARO Infrastructure Proposal

PIR

MURI Product: Uncertainty Alignment—Data Association—Fuzzy Graph Matching Sub‐processUser / Recipient: ARL’ Mr. Mark Thomas

Advanced All‐sourceSensor Fusion (A2SF)

‐Code and Knowledge transfer for Natural Language Processing to PFI‐Develop bench marking criteria for natural language processing (This will be used to compare Orbis vs. MURI NLP)‐Code and knowledge transfer for Graph matching to PFI‐Develop bench marking criteria for Global Graph Entity, Relationship, andAutomatic vs Manual search features (this will be used to compare MURI graphmatching, Story Teller Fuzzy search, Orbis CTA, and 21CT Graph analytics)

• A2SF In‐Process Discussions

MURI Role: V2 Analytics Support

Milan Patel

US‐UK Intl Technology Alliance Pgm

• Related to Both OSD Data‐to‐Decision (D2D) and ITA Pgm Tech Transition Discussions Sept 27‐29, 2011:

– 1) Networked & Information Science ITA & UK MoD D2D ‐related Efforts Mr. Gavin Pearson (UK DSTL) 

– 2) ARL D2D Initiative Dr. Barbara Broome (US ARL‐Computational Information Science Directorate) 

– 3) Goal‐driven and Data‐driven Processing, Dr. Cheryl Bolstad for Dr. Mica Endsley (Situation Awareness Technologies) 

– 4) NSF Center for Surveillance Research & ARO MURI on "Value‐centered Information Theory for Adaptive Learning, Inference, Tracking, and Exploitation" Prof. Randolph Moses (Ohio State University)

– 5) Network Science CTA D2D‐related Efforts Dr. Lance Kaplan (US ARL‐Sensors & Electrons Device Directorate), Prof. Tarek Abdelzaher (University of Illinois Urbana‐Champange), and Dr. Jin‐Hee Cho, (US ARL‐Computational Information Science Directorate)

And possibly others

Summary

• Concept of Operations– Army Principles for COIN operations studied and understood sufficiently well to:

• initiate Basic Research studies in various Discovery and Learning paradigms to assess and implement in Year 3 + efforts

• Initiate a design for a Multi‐Paradigm Toolkit that will form a back‐end analysis capability in Year 3+ efforts

• Technology Transition– Some transitions have successfully occurred in both Year 1 and Year 2

– A Variety of initiatives are in motion and evolving that have positive implications for Year 3 and onward 

Back‐ups

TransitionReady

Handoff to XXXXProvide Transition 

Support

No

YesExisting MURI

Research Prototype

Assess • Functional Fit

• Pre‐test Reqmts

Added ValueTo 

XXXX?

Next

Define:• Functional Mods• Arch/Infra Mods

• Test Plan

ProposalTo ARO

DEVELOP:ModifiedCapability

Building the Deductive Knowledge Base for Effects Modeling‐2*

*  Kott, A. and Corpac, P.S., COMPOEX Technology To Assist Leaders in Planning And Executing Campaigns In Complex Operational Environments in Proc of the 12th International Command and Control Research and Technology Symposium, June 2007

*  Levis, A.H., An Architecture for Effects Based Course of Action Development, Paper presented at the RTO SCI Symposium on “System Concepts for Integrated Air Defense of Multinational Mobile Crisis Reaction Forces”, held in Valencia, Spain, 22‐24 May 2000, 

Weak A Priori Deductive Knowledge(Second order Uncertainty)

COURSE OF ACTION DEVELOPMENT AND OPTIMIZATION*

Outcome/Effects Modeling Knowledge Base

IntelligenceSection

Brigade Combat Team

Commander

ISRSynchronization

Plan

Intelligence

LOEDesired Effects

MOE’s‐‐Measurable‐‐Tangible

Operations Lines of Effort

PIRRunning Estimate

Functional Area Officers‐‐Governance

‐‐Essential Services‐‐etc

“The only mission of the Intelligence Sectionis to answer the commander’s PIR “

PIR Development‐‐Nominate, iterate

SIGINTHUMINT

IntelligenceSection

IMINTOPSINT

SIGSUMHUMSUM IMSUMOPSSUM

CollectionSection

ProductionSection

All‐sourceAnalysts

Tag to PIR

PIRSection

All‐sourceAnalysts

ExternalSources

PIRWorkingGroup

‐‐Intell; all INTs‐‐LOE‐‐Operations

PIR Gaps

ISR Synch Plan

Officer Title  MOS Code Skills All‐Source Intelligence Officer 

35D  All MI officers receive initial and advanced training as a 35D. Duties include directing, supervising, and coordinating the planning, collection, processing, production, and dissemination of all‐source intelligence (HUMINT, IMINT, MASINT, SIGINT, OSINT, and CI) at all echelons. They perform multidiscipline collection management, coordination of surveillance and reconnaissance activities, and provide advice on the use of intelligence resources at all echelons. They supervise and perform IPB using automated intelligence data processing systems and advise the commander and subordinate units on the enemy, weather, and terrain. 

    

Table 1: IPB vs. PBA IPB PBA

Intel-centric Commander-centric Product-centric Process-centric Structured, iterative process Dynamic Process Focuses on red as an independent actor Emphasizes red-blue-gray interaction and

interdependencies Describes adversary’s courses of action Emphasizes commander’s anticipation and

pre-emption of the adversary through decisive effects

Focuses on courses of action initiated after inception of conflict/crisis  Generally delimited by specific geographical boundaries (operational and/or tactical levels)

Enables the commander to shape the pre- post-conflict environment to his advantage; reduces uncertainty during conflict/crisis Provides continuity of awareness from the strategic to the tactical level of operation

Focuses on target identification / definition Focuses on commander’s decisions to produce decisive effects in the battlespace

Stovepiped processes and information-flow Horizontally, vertically integrated processes; ubiquitous information through the publish-subscribe-broker information architecture

Chung, W., et al, Identifying and Tracking Dynamic Processes in SocialNetworks

Das Sarma, A., et al, Dynamic Relationship and Event Discovery

Set of Target Graphs depicting Complex Relationships

Of Interest

Hard‐Soft IncrementalEvidential Graph PIR Indicator‐Graphs

MOS 35D

VisualizationQuery‐Formulation Support

SNA  Tool

GM/Learning Tool

PIR, Pattern Assertions

Multi‐Paradigm Toolkit

Weak A Priori Deductive Knowledge(Second order Uncertainty)

Sequential Decision‐MakingUnder Strict Uncertainty(~Black Swan conditions)